U.S. patent number 7,801,591 [Application Number 11/641,268] was granted by the patent office on 2010-09-21 for digital healthcare information management.
Invention is credited to Vladimir Shusterman.
United States Patent |
7,801,591 |
Shusterman |
September 21, 2010 |
Digital healthcare information management
Abstract
System for diagnosis, medical decision support, and healthcare
information management that performs analysis of serial health
data, adapts to the individual data, and represents dynamics of the
most significant parameters (indicators), using at least two
scales. The system uses the first-scale (low-resolution) analysis
of a snapshot measurement of at least one indicator (primary
element) such as heart rate or blood pressure and uses a
second-scale (higher-resolution) analysis to determine serial
changes in each of the said primary elements. The system optimizes
information flow, usage of medical knowledge, and improves accuracy
of analysis of serial changes, and adaptability to each
individual's data. The information can be distributed in parallel
to separate databases at different locations.
Inventors: |
Shusterman; Vladimir
(Pittsburgh, PA) |
Family
ID: |
42733967 |
Appl.
No.: |
11/641,268 |
Filed: |
December 20, 2006 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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10816638 |
Apr 2, 2004 |
7343197 |
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10124651 |
Apr 17, 2002 |
6925324 |
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09583668 |
May 30, 2000 |
6389308 |
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Current U.S.
Class: |
600/509;
600/300 |
Current CPC
Class: |
A61B
5/7264 (20130101); G16H 50/20 (20180101); A61B
5/316 (20210101); A61B 5/0022 (20130101); A61B
5/366 (20210101); A61B 5/352 (20210101); G16H
50/70 (20180101); A61B 5/349 (20210101); G16Z
99/00 (20190201); A61B 5/0205 (20130101); A61B
5/411 (20130101); A61B 5/7232 (20130101); A61B
5/363 (20210101); A61B 5/7267 (20130101) |
Current International
Class: |
A61B
1/00 (20060101) |
Field of
Search: |
;600/300,508-523 |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
V Shusterman and O. Trofimov, Building and Application of Expert
Systems for Differential Diagnostics of Cardiovascular Diseases,
SAMS, 1994, vol. 14, pp. 15-24. cited by other .
William G. Baxt, MD et al., A Neural Network Aid for the Early
Diagnosis of Cardiac Ischemia in Patients Presenting to the
Emergency Department With Chest Pain, Annals of Emergency Medicine,
Dec. 2002, pp. 575-583. cited by other .
Hongmei Yan et al., The internet-based knowledge acquisition and
management method to construct large-scale distrbuted medical
expert systems, Computer Methods and Programs in Biomedicine (2004)
74, pp. 1-10. cited by other.
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Primary Examiner: Getzow; Scott M
Attorney, Agent or Firm: Eckert Seamans Cherin &
Mellott, LLC Radack, Esq.; David V. Brownlee, Esq.; David W.
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATION
This is a continuation-in-part of application Ser. No. 10/816,638,
filed Apr. 2, 2004, which is a continuation-in-part of application
Ser. No. 10/124,651, filed Apr. 17, 2002, now U.S. Pat. No.
6,925,324, which was a continuation-in-part of application Ser. No.
09/583,668, filed May 30, 2000, now U.S. Pat. No. 6,389,308.
Claims
What is claimed is:
1. A method useful in healthcare information management comprising:
collecting at least one primary element as a snapshot present at
the time of recording of health data using at least one collection
method selected from one-time, periodic, quasi-periodic and
continuous monitoring, and electronically comparing said at least
one primary element with at least one reference value to detect
changes in said at least one primary element and thereby identify
any abnormal or unstable primary element (a first-level,
low-resolution analysis); and analyzing serial changes in said at
least one primary element of health data using a dynamic serial
analysis and processing unit employing at least one of the
following methods selected from mathematical decomposition,
mathematical modeling, computer modeling, signal processing,
time-series analysis, statistical analysis, and methods of
artificial intelligence for assessing changes in serial data,
orthogonal decomposition, non-orthogonal decomposition (independent
component analysis), multidimensional scaling based on non-metric
distances and mapping techniques, non-orthogonal linear mappings,
nonlinear mappings and other methods, that make use of projection,
re-scaling (change of variables), methods from the theories of
singularities, bifurcations, catastrophes, and dynamical systems,
and other statistical estimators, linear and nonlinear correlation,
analysis of variance, cluster analysis, factor analysis, canonical
analysis, regression and discriminant function analyses, and
probabilistic methods, Bayesian probability, Bayesian network,
Markov model, hidden Markov model, and Mahalanobis distance,
pattern recognition, fuzzy logic, neural networks, expert systems,
and hybrid artificial intelligence systems to provide detailed
characterization of serial changes in any abnormal or unstable
primary element (a second-level, higher resolution serial
analysis).
2. A method as set forth in claim 1 which includes distribution to
at least one computing device having access to medical knowledge on
the Internet to incorporate said medical knowledge into said serial
analysis.
3. A method as set forth in claim 1 which includes distribution to
at least one computing device having access to digitized medical
journals, books and other publications to incorporate information
from such journals, books and publications into said serial
analysis.
4. A method as set forth in claim 1 in which said collecting at
least one primary element and electronically comparing said at
least one primary element with at least one reference value to
detect changes in said at least one primary element and thereby
identify any abnormal or unstable primary element (a first-level,
low-resolution analysis) of health data is performed repeatedly
over time.
5. A method as set forth in claim 4 in which said healthcare
information is collected and analyzed substantially continuously
for a period in a range of at least several minutes to many
days.
6. A method as set forth in claim 1 that includes personalized
adaptation of diagnostic criteria.
7. A method as set forth in claim 1 which includes analyzing at
least one primary element in said data as a snapshot present at the
time of recording of health data and comparing said at least one
primary element with at least one reference value in a first-level,
low resolution using at least one method selected from mathematical
decomposition, mathematical modeling, computer modeling,
time-series analysis, pattern recognition, signal processing,
probabilistic methods, statistical analysis, and methods of
artificial intelligence to detect changes in said at least one
primary element and thereby identify any abnormal or unstable
primary element (low-resolution analysis).
8. A method as set forth in claim 1 in which said first-level
analysis and said second-level analysis exchange information using
a wireless communication device selected from at least one of a
cell phone, smart phone, PDA, Wi-Fi, and other types of
radio-transmitters and communication devices.
9. A method as set forth in claim 1 in which analyzing said data to
provide detailed characterization of serial changes in said
abnormal or unstable primary elements is selected from a
fuzzy-logic classifier and a dynamic neural network with at least
one neuron (unit) analyzing changes in at least one state of
activity of at least one physiological, biochemical, biophysical,
mechanical, and genetic system relative to at least one reference
value.
10. A method as set forth in claim 7 in which said at least one
reference value is represented by a relation (function,
distribution) between said reference value and at least one state
of at least one physiological, biochemical, biophysical,
mechanical, and genetic system.
11. A method as set forth in claim 1, in which said analyzing
serial changes is applied to physiological signals selected from at
least one of electrocardiogram, electroencephalogram,
magnetocardiogram, pulse oximetry, impedance, magnetic resonance
(MRI), computed tomography (CT), ultrasound, fluoroscopic, X-ray
imaging, stress-test, physical activity, clinical symptoms, chest
pain, shortness of breath, nausea, blood pressure, cardiac output,
vascular activity, temperature, respiration, cardiac, abdominal, or
breathing sounds, blood flow, hormonal concentration, physical
activity, blood tests, weight, heart rate, enzyme and protein
level, genetic, genomic, proteomic, metabolomic, and molecular
data, neural activity, electroencephalographic activity, and other
electrical, mechanic, sonic, biochemical, biophysical processes in
the human body, demographic, psychological, and environmental
data.
12. A method as set forth in claim 1 in which at least one of
information completeness and a probability of a certain disease is
tracked dynamically in at least one level of analysis.
13. A method as set forth in claim 1 in which at least one of
information completeness and a probability of a certain disease is
tracked dynamically in at least one database.
14. A method useful in healthcare information management
comprising: analyzing at least one primary element of health data
by electronically comparing a snapshot of data recorded respecting
said at least one primary element with at least one reference value
to detect changes in such primary element and thereby identify any
abnormal or unstable primary element (a first-level, low resolution
analysis); analyzing serial changes in said at least one primary
element using a dynamic serial analysis and processing unit
employing at least one of the following methods selected from
mathematical decomposition, mathematical modeling, computer
modeling, signal processing, time-series analysis, statistical
analysis, and methods of artificial intelligence for assessing
changes in serial data, orthogonal decomposition, non-orthogonal
decomposition (independent component analysis), multidimensional
scaling based on non-metric distances and mapping techniques,
non-orthogonal linear mappings, nonlinear mappings and other
methods, that make use of projection, re-scalin (change of
variables), methods from the theories of singularities,
bifurcations, catastrophes, and dynamical systems, and other
statistical estimators, linear and nonlinear correlation, analysis
of variance, cluster analysis, factor analysis, canonical analysis,
regression and discriminant function analyses, and probabilistic
methods, Bayesian probability, Bayesian network, Markov model,
hidden Markov model, and Mahalanobis distance, pattern recognition,
fuzzy logic, neural networks, expert systems, and hybrid artificial
intelligence systems to provide detailed characterization of any
serial changes in abnormal or unstable primary element (a
second-level, higher resolution analysis); and analyzing changes in
said at least one primary elements in a third level resolution,
using at least one dynamic analysis and processing unit, which
includes combining the analysis of primary elements with digitized
personal health data.
15. A method as set forth in claim 14 which includes distributing
data produced by said second level analysis to at least one
computing device having access to medical knowledge on the Internet
to incorporate such knowledge into said second level analysis.
16. A method as set forth in claim 15 in which said distribution
includes at least one computing device having access to digitized
medical journals, books and other publications to incorporate
medical knowledge from said publications into said second level
analysis.
17. A method as set forth in claim 14 in which said collecting at
least one primary element and electronically comparing said at
least one primary element with at least one reference value to
detect changes in said at least one primary element and thereby
identify any abnormal or unstable primary element (a first-level,
low-resolution analysis) of health data is performed repeatedly,
seriatim over a prolonged period.
18. A method as set forth in claim 14 in which health data are
analyzed substantially continuously for a period in a range of at
least several minutes to many days.
19. A method as set forth in claim 14 in which said analyzing a
plurality of primary elements in said data in first-level low
resolution is selected from at least one of mathematical
decomposition, mathematical modeling, computer modeling,
time-series analysis, pattern recognition, signal processing,
probabilistic methods, statistical analysis, and methods of
artificial intelligence.
20. A method as set forth in claim 14 in which analyzing said data
to provide detailed characterization of serial changes in said
abnormal or unstable primary elements is performed using at least
one wireless communication device selected from at least one of a
cell home, smart phone, PDA, Wi-Fi, and other types of
radio-transmitters and communication devices.
21. A method as set forth in claim 14 in which analyzing said data
to provide detailed characterization of serial changes in said
abnormal or unstable primary elements is selected from a
fuzzy-logic classifier and a dynamic neural network with at least
one neuron (unit) analyzing changes in at least one state of
activity of at least one physiological, biochemical, biophysical,
mechanical, and genetic system relative to at least one reference
value.
22. A method as set forth in claim 14 in which said reference
values are represented by a relation (function, distribution)
between said reference values and at least one state of at least
one physiological, biochemical, biophysical, mechanical, and
genetic system.
23. A method as set forth in claim 14 in which at least one of said
first, second and third level analysis is applied to physiological
signals selected from at least one of electrocardiogram,
electroencephalogram, magnetocardiogram, pulse oximetry, impedance,
magnetic resonance (MRI), computed tomography (CT), ultrasound,
fluoroscopic, X-ray imaging, stress-test, physical activity,
clinical symptoms, chest pain, shortness of breath, nausea, blood
pressure, cardiac output, vascular activity, temperature,
respiration, cardiac, abdominal, or breathing sounds, blood flow,
hormonal concentration, physical activity, blood tests, weight,
heart rate, enzyme and protein level, genetic, proteomic, and
molecular data, neural activity, electroencephalographic activity,
and other electrical, mechanic, sonic, biochemical, biophysical
processes in the human body, demographic, psychological, and
environmental data.
24. A method as set forth in claim 14 in which at least one of
information completeness and a probability of a certain disease is
tracked dynamically in at least one level of analysis.
25. A system useful in healthcare information management
comprising: a first analysis and processing unit for analyzing a
snapshot of at least one of a plurality of primary elements from
recorded health data and processing said at least one primary
element to generate data respecting said at least one primary
element, and comparing at least one reference value respecting said
at least one primary element with data newly received by said first
analysis and processing unit and producing at least one indicator
respecting any differences between said at least one reference
value and said newly received data (a low resolution analysis), a
second analysis and processing unit for processing health data
collected over time using at least one of the following methods
selected from mathematical decomposition, mathematical modeling,
computer modeling, signal processing, time-series analysis,
statistical analysis, and methods of artificial intelligence for
assessing changes in serial data, orthogonal decomposition,
non-orthogonal decomposition (independent component analysis),
mathematical modeling, computer modeling, signal processing,
time-series analysis, statistical analysis, multidimensional
scaling based on non-metric distances and mapping techniques,
non-orthogonal linear mappings, nonlinear mappings and other
methods that make use of projection, re-scaling (change of
variables), methods from the theories of singularities,
bifurcations, catastrophes, and dynamical systems, and other
statistical estimators, linear and nonlinear correlation, analysis
of variance, cluster analysis, factor analysis, canonical analysis,
regression and discriminant function analyses, and probabilistic
methods, Bayesian probability, Bayesian network, Markov model,
hidden Markov model, and Mahalanobis distance, pattern recognition,
fuzzy logic, neural networks, expert systems, and hybrid artificial
intelligence systems to detect serial changes in said at least one
primary element (higher resolution analysis); and a communications
unit for exchanging information between said first and second
analysis and processing units.
26. A system as set forth in claim 25 which includes at least one
acquisition unit for collecting data using at least one collection
method selected from one-time, periodic, quasi-periodic and
continuous monitoring.
27. A system as set forth in claim 25 in which said communications
unit is adapted to distribute data to at least one computing device
having access to medical knowledge on the Internet to incorporate
said medical knowledge into said higher resolution analysis.
28. A system as set forth in claim 25 which includes a third level
of resolution that comprises personalized adaptation of diagnostic
criteria.
29. A system as set forth in claim 25 in which said first analysis
and processing unit and said at least one computer device for
analyzing a snapshot of at least one of a plurality of primary
elements use at least one of the following methods selected from
mathematical decomposition, mathematical modeling, computer
modeling, signal processing, time-series analysis, statistical
analysis, and methods of artificial intelligence for assessing
changes in serial data, orthogonal decomposition, non-orthogonal
decomposition (independent component analysis), multidimensional
scaling based on non-metric distances and mapping techniques,
non-orthogonal linear mappings, nonlinear mappings and other
methods, that make use of projection, re-scaling (change of
variables), methods from the theories of singularities,
bifurcations, catastrophes, and dynamical systems, and other
statistical estimators, linear and nonlinear correlation, analysis
of variance, cluster analysis, factor analysis, canonical analysis,
regression and discriminant function analyses, and probabilistic
methods, Bayesian probability, Bayesian network, Markov model,
hidden Markov model, and Mahalanobis distance, pattern recognition,
fuzzy logic, neural networks, expert systems, and hybrid artificial
intelligence systems.
30. A system as set forth in claim 25 in which at least one of said
first analysis and processing unit and said at least one computer
device analyze physiological data selected from at least one of
electrocardiogram, electroencephalogram, magnetocardiogram, pulse
oximetry, impedance, magnetic resonance (MRI), computed tomography
(CT), ultrasound, fluoroscopic, X-ray imaging, stress-test,
physical activity, clinical symptoms, chest pain, shortness of
breath, nausea, blood pressure, cardiac output, vascular activity,
temperature, respiration, cardiac, abdominal, or breathing sounds,
blood flow, hormonal concentration, physical activity, blood tests,
weight, heart rate, enzyme and protein level, genetic, proteomic,
and molecular data, neural activity, electroencephalographic
activity, and other electrical, mechanic, sonic, biochemical,
biophysical processes in the human body, demographic,
psychological, and environmental data.
31. A system as set forth in claim 25 in which said communication
unit is wireless.
32. A system as set forth in claim 25 in which said second analysis
and processing unit is connected to several computers via a
computer network for at least one of visualization and analysis of
the health data.
33. A system as set forth in claim 25 in which said first analysis
and processing unit is connected to several personal devices
selected from noninvasive and implantable devices for data
acquisition and low-level analysis of health data.
34. A system as set forth in claim 25 in which said higher
resolution analysis of health data is performed using parallel
processing.
35. A system as set forth in claim 25 in which said higher
resolution analysis of health data is distributed among several
computers connected via specialized computer networks, selected
from networks for home use, work environment, hospital, and
transportation.
36. A system as set forth in claim 25 in which said higher
resolution analysis of health data is distributed among several
computers connected via at least one specialized computer network,
including networks for tracking serial changes in patients with at
least one condition selected from congestive heart failure,
coronary artery or ischemic heart disease, cardiac arrhythmias,
hypertension, syncope, asthma, diabetes, and other illnesses.
37. A system as set forth in claim 25 in which said higher
resolution analysis of health data is integrated into an artificial
intelligence system, which includes at least one method selected
from an expert system, a neural network and a combination of the
methods (a hybrid system).
38. A system as set forth in claim 32 in which said computer
network includes at least one of a fuzzy-logic classifier and a
dynamic neural network with at least one neuron (unit) for
analyzing changes in at least one state of activity of at least one
physiological, biological, biophysical, mechanical and genetic
system relative to at least one reference value.
39. A system as set forth in claim 25 in which said at least one
reference value is represented by a relation (function,
distribution) between said reference values and at least one state
of at least one physiological, biochemical, biophysical,
mechanical, and genetic system.
40. A system as set forth in claim 25 in which said second analysis
and processing unit is adapted to track dynamically at least one of
information completeness, disease state transition probability
matrix, observation (emission) probability matrix, Bayesian
probability, Bayesian network, Markov model, hidden Markov model,
and probability of a certain disease in all distributed databases
in at least one level of detail.
41. A system as set forth in claim 26 in which said communications
unit is adapted to continuously send at least one of information,
raw data, and derived parameters to said at least one of said
acquisition unit and said first and second analysis and processing
units.
42. A system as set forth in claim 25 in which said first analysis
and processing unit is portable.
43. A system as set forth in claim 26 in which said at least one
acquisition unit is implantable.
44. A system as set forth in claim 43 in which said implantable
acquisition unit includes processing capability.
45. A system as set forth in claim 25 in which at least one of said
first analysis and processing unit and said second analysis and
processing unit is a wireless communication device selected from at
least of one of a cell phone, smart phone, PDA, Wi-Fi, and other
types of radio-transmitters and communication devices.
46. A system useful in healthcare information management comprising
a dynamic serial analysis and processing unit for processing health
data collected over time using at least one of the following
methods selected from mathematical decomposition, mathematical
modeling, computer modeling, signal processing, time-series
analysis, statistical analysis, and methods of artificial
intelligence for assessing changes in serial data, orthogonal
decomposition, non-orthogonal decomposition (independent component
analysis), multidimensional scaling based on non-metric distances
and mapping techniques, non-orthogonal linear mappings, nonlinear
mappings and other methods, that make use of projection, re-scaling
(change of variables), methods from the theories of singularities,
bifurcations, catastrophes, and dynamical systems, and other
statistical estimators, linear and nonlinear correlation, analysis
of variance, cluster analysis, factor analysis, canonical analysis,
regression and discriminant function analyses, and probabilistic
methods, Bayesian probability, Bayesian network, Markov model,
hidden Markov model, and Mahalanobis distance, pattern recognition,
fuzzy logic, neural networks, expert systems, and hybrid artificial
intelligence systems to detect serial changes in said at least one
primary element (serial analysis) to provide detailed
characterization of any abnormal or unstable primary elements.
47. A system as set forth in claim 46 which includes a low
resolution analysis and processing unit for analyzing a snapshot of
said at least one primary element in low resolution and a
communications unit for distributing data respecting said primary
elements to at least one computing device for exchanging
information between said low resolution analysis unit and said
dynamic analysis unit.
48. A system as set forth in claim 46 which includes at least one
acquisition unit for collecting health data.
49. A method useful in healthcare information management
comprising: collecting at least one primary element over a period
of time; and analyzing serial changes in said at least one primary
element of health data using a dynamic serial analysis and
processing unit employing at least one of the following methods
selected from mathematical decomposition, mathematical modeling,
computer modeling, signal processing, time-series analysis,
statistical analysis, and methods of artificial intelligence for
assessing changes in serial data, orthogonal decomposition,
non-orthogonal decomposition (independent component analysis),
multidimensional scaling based on non-metric distances and mapping
techniques, non-orthogonal linear mappings, nonlinear mappings and
other methods, that make use of projection, re-scaling (change of
variables), methods from the theories of singularities,
bifurcations, catastrophes, and dynamical systems, and other
statistical estimators, linear and nonlinear correlation, analysis
of variance, cluster analysis, factor analysis, canonical analysis,
regression and discriminant function analyses, and probabilistic
methods, Bayesian probability, Bayesian network, Markov model,
hidden Markov model, and Mahalanobis distance, pattern recognition,
fuzzy logic, neural networks, expert systems, and hybrid artificial
intelligence systems to provide detailed characterization of serial
changes in any abnormal or unstable primary element.
50. A method as set forth in claim 49 in which analyzing said
serial changes is performed using a wireless communication device.
Description
FIELD OF THE INVENTION
This invention relates to the field of medical information
management, diagnosis, and decision support and more specifically
to a method and system for analyzing medical or health data and its
serial changes, combining general medical knowledge with individual
subject's data and its serial changes, and optimizing the
information flow, structuring and representing the results.
BACKGROUND OF THE INVENTION
Medical decision process has been traditionally considered
unstructured and ill-posed. Indeed, the ill-posed nature of medical
diagnosis and decision making has given rise to the perception that
medical diagnosis is a form of art, which cannot be quantified or
structured. The main difficulties of medical decision making are
related to the following key issues.
First, the nature of medical information processing is inherently
probabilistic with a large number of possible diseases, disease
stages, side-effects, complications, etc. These possibilities can
be alternative, additive, complementary, correlated, partially
correlated, or uncorrelated. For example, a pain in the chest area
can be caused by heart disease, stomach ulcer, back problems,
hypochondriac, neurological conditions, or various combinations of
these disorders. In addition, a person might have a combination of
different diseases that are not related to the symptoms being
investigated but, nevertheless, might change the patient's symptoms
and obscure diagnosis. For example, a combination of heart angina
and back pain might be difficult to differentiate, because both
diseases might have similar symptoms.
Second, there is enormous individual variability in the expression
of diseases, which creates completely different profiles of the
same illness in different subjects. For example, myocardial
infarction (heart attack) can be manifested by pain in the upper
left area of the chest, the central region of the chest, left arm,
back, or shortness of breath.
Third, incompleteness of information represents a significant
problem in medical decision making. In particular, information
about different diagnostic tests performed at different times can
be distributed among different databases located in different
medical institutions. For example, a surgical procedure performed
two years ago can be located in that hospital's database, whereas
subsequent tests were performed in a different hospital and are
located in that hospital's database. Some of the local databases
distributed among different medical institutions may be temporarily
or permanently unavailable. Thus, a mechanism is needed to estimate
the total information completeness, and this information
completeness needs to be tracked dynamically, as new information
becomes available over time.
Due to these reasons, the "art" of medical diagnosis has
traditionally been considered as an ability to weight all probable
causes of illness in the shortest possible time in order to start
an appropriate treatment as early as possible.
Recent developments of computer and network technologies have
created a technological background for incorporation of the
ill-posed medical decision making rules and facts into computer and
network algorithms. A number of studies have examined this problem,
using statistical analysis, pattern recognition, neural networks,
and expert systems. For example, application of methods of
artificial intelligence for medical diagnosis have been described
by Shusterman et al. in Building an application of Expert Systems
For Differential Diagnostics of Cardiovascular Diseases, SAMS,
1994, Vol. 14, pp. 15-24, Yan et al. in The Internet-based
Knowledge Acquisition and Management Method to Construct
Large-scale Distributed Medical Expert Systems, Comput Methods
Programs Biomed. 2004 April; 74(1): 1-10, and Baxt et al. in A
Neural Network Aid for the Early Diagnosis of Cardiac Ischemia in
Patients Presenting to the Emergency Department with Chest Pain,
Annals of Emergency Medicine, December 2002 40:06, among other
publications.
Various techniques for computerized identification and analysis of
health data are also described in several United States patents.
For example, Barnhill et al. in the U.S. Pat. No. 6,882,990, (2005)
discloses methods of identifying biological patterns using multiple
data sets. Using learning process on the training data, optimal
solutions are determined for the identification of patterns that
are important for medical diagnosis, prognosis and treatment. Bardy
in the U.S. Pat. No. 6,887,201 (2005) describes system and method
for determining a reference baseline of regularly retrieved patient
information for automated remote patient care. The method uses a
database of patient records to determine a set of reference
measures. Asada et al. in U.S. Pat. No. 5,463,548 (1995) disclose a
method and system for differential diagnosis based on clinical and
radiological information using artificial neural networks. The
method uses radiographic data and clinical information to
differentiate mammographic images and lung diseases. Leatherman in
the U.S. Pat. No. 5,544,044 (1996) discloses a method for
evaluation of health care quality using analysis of health care
claims records to assess the quality of care based on conformance
to nationally recognized medical practice guidelines or quality
indicators and to provide a means to supplement claims with data
from patient medical records. Iliff in the U.S. Pat. Nos. 5,594,638
(1997), 5,868,669 (1999), 6,113,540 (2000), 6,206,829 (2001),
6,482,156 (2002), and 6,849,045 (2005) disclose systems and methods
for providing computerized, knowledge-based medical diagnostic and
treatment advice. "Meta" functions for pattern matching and
time-density analysis are included to determine the similarity and
the number of medical complaints per unit of time. A re-enter
feature monitors the user's changing condition over time. A symptom
severity analysis helps to respond to the changing conditions.
System sensitivity factors may be changed to adjust the system
advice as necessary. Zimmerman in the U.S. Pat. No. 5,941,820
(1999) discloses a method for measuring patient data, determining
statistics from the data, variation within the data, homeostasis,
modifying control chart limits based on the measure of homeostasis
and displaying the statistic on the modified control chart. The
control charts are modified as data varies over time. By
determining the amount of consistency or similarity using
autocorrelation or serial correlation, significant changes are
identified. Herren et al. in the U.S. Pat. No. 6,108,635 (2000)
discloses a system for drug discovery, design of clinical trials,
performing pharmacoeconomic analysis, and illustrating disease
progression over time. Freedman in the U.S. Pat. No. 6,126,596
(2000) discloses a system for collecting data and using these data
for diagnosis and lookup of appropriate treatments. Barry et al. in
the U.S. Pat. No. 6,188,988 (2001) disclose systems, methods and
computer program products for guiding the selection of treatment,
which comprise (a) providing patient information to a computing
device (a knowledge base and expert rules for selecting treatment
and advisory information; (b) generating a listing of treatments;
and (c) generating advisory information. Papageorge in the U.S.
Pat. No. 6,584,445 (2003) discloses a computerized health
evaluation system for joint patient-physician decision making. The
system includes a patient input module, a physician input module,
and a database of medical information about diseases. The computer
system uses an algorithm for weighing the patient data and the
physician data and generating a report with various treatment
options. Sadeghi et al. in the U.S. Pat. No. 6,687,685 (2004)
discloses a system and method for automated medical
decision-making, such as online, questionnaire-based medical
triage. Information is modeled in a Bayesian Network, and the
conditional probability may be determined in a real-time.
SUMMARY OF THE INVENTION
This invention provides a method and system that can be used for at
least one of information management, decision support, and
diagnosis. The method and system distribute (structure) the
information into at least two levels of detail (scales or
resolutions). A low-resolution scale represents a snapshot
measurement of at least one indicator (vital sign or primary
element) such as heart rate or blood pressure. A higher resolution
scale is designed to determine serial changes in each of the said
primary elements. Low, intermediate and high-resolution scales can
exchange information between each other for improving the analyses;
the scales can be distributed vertically among the units connected
by a network and defined according to the corresponding software
and hardware resources. Uncertainty or probability of a diagnosis
is tracked dynamically (the probabilities are updated periodically
or quasi-periodically over time taking into account information
available at each time point; new information is included in the
analysis as it becomes available) based on the information
availability or completeness relative to the total complete
information at each level and at multiple levels. This structuring
provides several advantages. First, it improves and optimizes the
flow of information along the network. This feature is significant,
since the volume of information provided by a multitude of
diagnostic tests is high (such as electrocardiographic monitoring,
magnetic resonance imaging (MRI), computer tomography (CT),
CAT-scans, echocardiography, biochemical, and other tests) and
increases with time. The structuring permits control this high
volume of information, so the most important information (vital
signs) is analyzed on-line and on-site (Low-resolution), whereas
the rest of the information, which includes subtle changes in
patient's state, are detected and quantified using comparative
analysis of serial data (Higher level of resolution). Such
distribution of the enormous amount of medical information prevents
information overload and ensures that the information is processed
accurately and in a timely fashion, and allow medical professionals
to receive adequate and accurate information about the patient
tailored to the specific setting of the medical care and patient's
profile.
Second, this multi-level structure also ensures adaptability of the
system, in which the system processes all available data to learn
the individual patient's pattern of normal range and abnormal
variations. The adaptability is achieved by collecting and
processing serial data at the higher scales and then, using this
information at the lower scale to individually tailor (edit,
adjust) the diagnostic and processing criteria (thresholds). Third,
for reasons described above, this multi-level structure also
optimizes bi-directional communication and personalized and timely
advice and treatment of each patient.
Thus, by vertically distributing the analyses and representation in
several levels, the system optimizes information flow, usage of
medical knowledge, and improves accuracy of analysis of serial
changes, and adaptability to each individual's data. Low,
intermediate and high-resolution scales can exchange information
between each other for improving the analyses; the scales can be
distributed among the units connected by a network and defined
according to the corresponding software and hardware resources. In
addition, the system can be adapted to optimize usage of medical
knowledge contained in medical journals, books, the Internet, and
other materials for personalized analysis of serial data. The
system optimizes and improves the information flow by vertically
distributing it into several levels or Scales according to the
importance and relevance of the information, and according to the
available software and hardware resources. The low-resolution Scale
I represents one-time, periodic, or quasi-periodic snapshot
measurements of health data, such as heart rate, blood pressure,
blood count, cardiac output, physical activity, temperature, and
weight, referred to as the primary elements. The higher-resolution
Scale II is used to analyze serial changes in each of these primary
elements. Optionally, the 3.sup.rd scale can be used to analyze
combined serial changes of these primary elements. By using this
personalized analysis, the system improves accuracy and clarity of
analysis and representation of personalized serial analysis. These
scales can also include medical knowledge from medical textbooks,
journals, and other materials available on the computer network to
improve personalized analysis.
Examples of such a multi-scale structure for analysis,
representation, distribution and management of health data is
presented in FIG. 15. As depicted in the figure, in the first
(bottom) scale, data is collected from at least one, and
preferably, a multitude of diagnostic devices, such as
electrocardiographic, electroencephalographic, echocardiographic,
magnetocardiographic, magnetic resonance imaging, computer
tomography, thermometer, blood pressure tonometer, pulse oxymeter,
impedance meter, genetic/DNA/genotype/proteomics/metabolomics
measurements, MRI, CT, ultrasound, fluoroscopic, X-ray image,
stress-test, physical activity test, neurographic recordings,
biochemical tests, blood tests, enzyme tests, clinical symptoms,
such as chest pain, shortness of breath, nausea, etc. These data
can be collected as a one-time test, periodic, quasi-periodic, or
continuous monitoring (measurements). At the low-resolution level
(scale) I, these data are processed to extract the most important
indicators (vital signs, diagnostic indicators) or primary
elements, such as heart rate, blood pressure, magnitudes and
durations of electrocardiographic waves (QRS, T, and P-waves, and
ST-segment, T-wave alternans), cardiac output, respiration,
temperature, neural activity, etc.
At the next level (scale) II, dynamics of each primary element
(vital sign or diagnostic indicator) is analyzed using serial
recordings obtained from the individual. The dynamical (serial)
analysis is performed using the mathematical, modeling,
probabilistic, pattern-recognition, time-series, signal-processing,
statistical, computer, and artificial intelligence methods
described below. In the simplest-case scenario, serial changes are
analyzed using simple statistical parameters, such as the mean or
median value, or the standard deviation (a square root of
variance), or a range of variations (for example, 25%-75% range) of
the time series of serial changes over a certain time interval. The
serial changes in any of these statistical parameters or in the
combination of these parameters can be estimated, for example,
using a statistical test that determines the statistical
significance of serial changes over time (for example, a
non-parametric, Friedman ANOVA for repeated measurements or a
paired t-test, or an ANOVA for repeated measurements), or using
pre-selected or adaptive thresholds (for example, a threshold of 3
standard deviations can be used to detect significant changes in
the mean values). As a result of this dynamic analysis, trends of
changes are represented either as quantitative data, qualitative
information, an advice, or graphs of trends in genetic, genomic,
proteomic, electrocardiographic, echocardiographic, neurographic
(neural), electroencephalographic, magnetocardiographic,
magnetoencephalographic, magnetic resonance (MRI), computer
tomography (CT) and X-ray imaging. The results of analysis can be
also color-coded, for example, if an indicator is within a normal
range or within a certain percent of a moving average of previous
values, it will be highlighted with a green color. A borderline
parameter can be highlighted by yellow color, and a parameter
beyond 3 standard deviations from normal range can be highlighted
by red color.
The results of dynamic analysis performed at Scale (level) II are
sent to the next, third level of processing. They are also sent to
the Level Ito personalize (adjust, adapt, individually tailor) the
diagnostic thresholds. For example, the threshold for detection of
tachycardia can be lowered if the subject's individual heart rate
during the last several days was slow. Or the threshold for
detection of QT-prolongation could be lowered if the subject is
taking antriarrhythmic drugs that prolong QT interval.
When the information is transferred to the Level III, dynamics of
each vital sign (primary element, diagnostic indicator) is
integrated to generate a combined personalized dynamics that
includes changes (trends) of various diagnostic indicators.
Combining the information or using parameter fusion (when several
parameters are combined into a single, composite parameter)
improves the diagnostic value of the information, since a
combination of parameters can help to achieve a more accurate
diagnosis. For example, combination of trends of heart rate and
T-wave alternans can be used to determine at which level of heart
rate T-wave alternans increase and at which level of heart rate
T-wave alternans disappears. Another example is a combined analysis
of changes in heart rate and QT-intervals, which allows determining
a personalized relationship between these two values. This combined
information can be useful for determining an optimal treatment
strategy, for example, whether or not the level of T-wave alternans
at a given heart rate is abnormal and should be controlled, for
example, by implanting an implantable cardioverter-defibrillator
(ICD). The results obtained using this combined analysis at Level
III are sent to the higher scale and to the lower scales II and I
for individual tailoring (personalized adaptation or adjustment) of
diagnostic criteria (thresholds).
At Level IV, the results of information processing performed at
lower levels I-III are compared with medical knowledge available in
medical textbooks, scientific journals, databases, Internet,
networks, and libraries, including statistical data, guidelines,
and case studies to determine possible diagnoses. The comparison
with medical knowledge can be performed using statistical analysis,
pattern recognition, artificial intelligence, neural networks,
expert systems, mathematical decomposition, mathematical
transformation, or mathematical modeling or computer modeling. As a
result of this comparison, a list of possible causes of patient's
symptoms is determined along with the probability of each
diagnosis. This information is sent to the next, Level V, which
determines the most probable diagnosis.
Note that the multi-scale (multi-layer) structure can be compressed
into fewer (even 2) scales (that can be implemented in the a single
microprocessor, computer, cell phone, PDA, smart phone,
microcontroller) or expanded into more scales (which can be also
distributed among several different parallel or hierarchical
databases connected via network or Internet), depending on the
specifics of a clinical setup, available hardware and software
resources, and depending on the specifics of an individual patient
health status and personal profile, including age, diagnosis,
disease stage, etc. It is also possible to use any number or
combination of the above-described (or similar) levels (layers,
scales). For example, a specific diagnostic structure can be used
for subjects with chronic congestive heart failure with a typical
profile of a low ejection fraction, a low tolerance to physical
activity, relatively high resting heart rate and low heart rate
variability. Among the parameters that could be modified for such
patients is a narrow range of normal heart rate variations. At each
scale, the analysis can use at least one of statistical methods,
probabilistic methods, Bayesian models/networks, Markov models or
hidden Markov models, pattern recognition, artificial intelligence,
neural networks, expert systems, mathematical decomposition,
mathematical transformation, or mathematical modeling or computer
modeling.
FIG. 16 shows another variant of multi-scale structure, in which
the 1 st level, low-resolution analysis is implemented together
with each diagnostic sensor, so that collecting health data and
processing these data in a low-resolution, 1.sup.st level analysis
is done at the same place, in a real-time. The collected heath data
and/or the results of 1.sup.st-level processing are then sent to
the 2.sup.nd level processing, possibly, via Bluetooth, other
radio-transmitters, cell phone, Wi-Fi or other networks. The
2.sup.nd level processing, as explained earlier, includes analysis
of serial changes, using the information obtained previously from
the same subject, and sends the results of analysis back to the
1.sup.st level to optimize diagnostic and monitoring
thresholds.
FIG. 17 shows yet another version of a multiscale structure, in
which Scale 2 analysis is also distributed among different
locations. The 2'' scale analysis can be implemented on-site within
the same diagnostic unit that collects health data and performs
1.sup.st scale analysis. Alternatively, the 2.sup.nd scale analysis
can be implemented at a different physical location, or distributed
among several different locations, AS FIG. 17 shows.
Note also that the multi-scale structure can be further expanded in
horizontal direction, to include different modules of support for
different groups of diseases (for example, modules for
cardiovascular, neurological, gastroenterological, infectious
disease), different patient populations (heart failure, renal
failure, chronic obstructive lung disease, elderly, etc.),
different groups of medications (anti-arrhythmic, beta-blockers,
etc), different device treatments (implantable cardiac devices,
hemodialysis, etc.), different medical settings (ambulatory,
in-hospital, out-of-hospital, military, mass emergency situations,
terrorist threats, weapons of mass destruction alerts).
The multi-scale structure can be implemented in various
combinations of computing devices, such as cell phones, specialized
processors, personal digital assistant (PDA), smart phone, personal
computer, a computer network or specialized networks. It is
possible, for example, to implement the first 2 or 3 scales in a
miniaturized, personal system (for example, implemented in a cell
phone or a personalized monitoring system) that a person carries
around, whereas the higher levels are implemented in a computing
device that is located remotely and communicates with the lower
levels by using wireless communication (cell phone, GPS, GPRS,
Internet, Wi-Fi, etc.). Other combinations of scales implemented
locally or at remote locations are also possible. Preferably, the
higher-level analysis is performed on a powerful computer device,
such as a computer server, which has a database of serial data from
each subject for comparative analysis, and also a database of
medical knowledge of characteristics of different diseases. Another
example of implementation of a multi-level structure is a home
system, which includes sensors (can be embedded in home appliances,
such as bed, chairs); lower and higher-level processing units
implemented in a home computer (which can also communicate
information to and from an individual via a TV or radio or cell
phone) and a higher-level processing (connected via Internet or
specialized network) implemented in a medical center. Yet, another
example of implementation of a multi-level structure is a car-based
system, which includes sensors for physiological monitoring or
periodic checkups, (i.e. sensors for monitoring heart rhythm could
be incorporated in the armchair; other sensors might be activated
and attached to the human body whenever necessary). The sensors are
connected with the car's computer (the connection could be
wireless, via Bluetooth or Zigbee), so that the computer can
perform the scale processing or both, the 1.sup.st and 2.sup.nd
scale processing. Alternatively, the sensors can communicate
directly with a cell phone, which performs the 1.sup.st or 1.sup.st
and 2.sup.nd scale processing. The cell phone (or the car computer)
can be connected wirelessly (via a cell phone, GPS, or Internet)
with a remote computer (which contains a database of this person's
serial recordings) for a higher-level processing. Each of these
processing levels has a bi-directional communication with other
levels for exchanging information, individual tailoring of
monitored parameters, providing advice or warnings to the
individual in the car or sending an alarm/notification to the
individual's physician or nurse via a cell phone or remote
computer.
The above-described structure can be used for forecasting
(prediction) of the trends in patient's status, including
forecasting high-risk periods for developing myocardial ischemia or
cardiac arrhythmias by analyzing changes in the pattern of
physiological indicators and determining periods when these
patterns become unusual (for example, exceeding 3 standard
deviations of normal range) or abnormal and, therefore, indicating
high-risk of a complication, such as myocardial infarction,
arrhythmia, or stroke. The prediction can be performed using at
least one of statistical methods, probabilistic methods, Markov
models, hidden Markov models, Bayesian network, pattern
recognition, artificial intelligence, neural networks, expert
systems, mathematical decomposition, mathematical transformation,
or mathematical modeling or computer modeling.
The above-described system can be also used to provide an advice or
a recommendation regarding changes in diet, stress management,
physical activity, treatment (for example, administering a drug or
implanting an implantable cardioverter-defibrillator or pacemaker
device), or a necessity of diagnostic test. The system can also be
used for bi-directional communication between individual subjects
(patients), medical centers, and medical professionals (physicians,
nurses, and technicians). The above-described system can be also
integrated into other information management systems, for example,
standard data management systems (such as hospital information
management systems developed by Epic Systems, Inc.). The system can
represent the results using at least one of quantitative
presentation for medical professionals and qualitative presentation
for a lay person who has no medical background. Structuring of the
analysis is achieved by constructing the at least two, and
preferably three, information scales that represent the most
significant parameters at different level of detail.
In the practice of this invention, health data is preferably
monitored on a substantially continuous, periodic, or
quasi-periodic basis, meaning that data are taken or read and
recorded periodically such as every few seconds, minutes, hours,
days or longer. The periodic recording of data may extend for short
periods such as a few minutes or days, or may extend for prolonged
periods of time such as weeks, months or longer. The data is
generally recorded seriatim or one after another. The data that is
recorded may be varied from time-to-time depending on the analysis
of data that is collected so as to collect data that may be more
relevant to changes in a subject's primary elements. Data is
recorded for doing low resolution analysis as well higher scale
analyses. As used herein "health data" is used generically to mean
all forms of data relating to health, including physiological data
that include but are not limited to blood pressure, cardiac output,
vascular activity, temperature, respiration, cardiac, abdominal, or
breathing sounds, blood flow, hormonal concentration, enzyme and
protein levels, genetic, proteomic, metabolomic, and molecular
data, neural activity, electroencephalographic activity, and other
electrical, mechanic, sonic, biochemical, and biophysical processes
in the human body, other information related to human life,
including demographic (age, gender), environmental (pollution, job
conditions), and psychological data, life styles, exercise
activities, etc.
In addition, this invention provides an easy-to-use system for
structured and complete analysis and representation of data and its
serial changes quantitatively for medical professionals.
Structuring of the analysis is achieved by constructing the at
least two, and preferably three, information scales that represent
the most significant parameters at different level of detail. The
multi-scale analysis and representation can be applied to all types
of health data defined above. The values of the data obtained from
individual patients can be compared with the average values
obtained in a group or a population of patients to facilitate
analysis of individual data and to determine the values that
characterize groups of patients with similar characteristics and/or
similar disorders.
A preferred embodiment of this invention further includes
implementation of the multi-scale analysis. Specifically, this
invention provides for the implementation of the multi-scale
analysis on a distributed network of personal devices (which may
include devices for registration and processing of
electrocardiogram, electroencephalogram, blood pressure, cardiac
output, temperature, respiration, vascular tone, blood glucose, and
other biochemical, biophysical, biomechanical, hormonal, molecular,
and genetic data) and centralized computers with a bi-directional
communication between them. This distributed network allows: 1)
uninterrupted data acquisition (continuous or discrete) anytime,
anywhere, 2) fast transmission of the acquired information to the
other computers on the network for processing and comparison with
previously acquired serial data (including individual baseline
data), 3) fast and accurate processing, analysis, and accurate
detection of serial changes, 4) transmitting the results back to
personal devices (held by the individuals and medical personnel) to
inform them and adjust the monitoring thresholds.
On the network, the data and its processing may be distributed
horizontally among the devices and computers according to the
computational resources, time period of data acquisition, type(s)
of a medical test(s), geographical location, professional and
living environment. For example, one distributed personal network
of devices and computers could be setup at home, a second network
could be setup at a work place, a third network could be setup in a
hospital, and a fourth one could be setup in a transportation
system (such as a train or an airplane), so that all four networks
are connected to each other and can exchange the information
instantly. The personal devices may include devices for acquisition
and analysis of electrocardiogram, electroencephalogram,
electromyogram, blood pressure, impedance, vascular resistance,
cardiac output, biochemical, genetic, proteomic, molecular, and
other types of health and environmental data.
The advantages of the distributed processing include: 1) a higher
computational power and speed of distributed parallel processing,
which allow efficient implementation of such computationally
expensive methods of artificial intelligence as neural networks,
expert systems, and hybrid artificial intelligence systems, and
other mathematical and statistical tools, and 2) fast exchange of
information among the devices on the network as well as between
different networks.
Low, intermediate and high-resolution scales are defined according
to the corresponding software and hardware resources. A
low-resolution (Scale I) represents a small number of the most
important primary elements such as intervals between the heart
beats, duration of PQ, QRS, and QT-intervals, amplitudes of P-, Q-,
R-, S-, and T-waves. This real-time analysis is implemented in a
portable device that requires minimum computational resources. The
set of primary elements and their search criteria are adjusted for
each physiological signal utilizing computational resources of
intermediate or high-resolution levels. At the
intermediate-resolution (Scale II), serial changes in each of the
said elements are determined using a mathematical decomposition
into series of orthogonal basis functions and their coefficients.
This scale is implemented using a specialized processor or a
computer organizer. At the high-resolution (Scale III), serial
changes in all elements of the ECG and their combinations are
extracted using orthogonal mathematical decomposition to provide
complete information about the dynamics of the signal. This scale
is implemented using a powerful processor, a network of computers
or the Internet. Scale I may be implemented in a portable,
pocket-size device, in which the signal is decomposed into a
plurality of primary elements and parameters such as intervals
between the heart beats, type of a cardiac complex, amplitudes and
duration of P-, QRS, T-, and U-wave, QT-interval, amplitude of
ST-segment. Scale I of the system provides the means for real-time
electrocardiographic analysis by comparing the primary elements of
ECG with reference values (individual thresholds) using the minimum
computational resources. The reference values are programmed into
the device based on normal values for the primary elements for the
patient. Scale I includes means for adjustment of individual
thresholds and criteria for rejection of noisy data. A detector of
noise and error rejects the noisy data if the primary elements
exceed physiologic range. Alternatively, modification of the
primary elements and adjustment of their search criteria can be
performed automatically at the higher-resolution Scale II or Scale
III. In this case, the Scale I analysis is implemented using a
programmable microprocessor that can be re-programmed at the
higher-resolution scales to account for the individual
characteristics of the physiological pattern and monitoring goals.
Specific sets of primary elements can be used for patients with
different cardiovascular abnormalities.
Scale I can be used in two modes: static mode and dynamic mode. The
static mode is used for one-time ECG examination in which the newly
acquired primary elements are compared with the default reference
values. The dynamic mode is used for comparison of the newly
acquired primary elements and waveforms with the primary elements
and waveforms that were previously acquired from the same person.
The shapes of QRS, T, and P-waves are compared using
cross-correlation function. A small magnitude of the difference
between the two measurements permits classifying them as
substantially similar and keeping only one measurement in the
memory.
Scale I provides sufficient information for standard, one-time,
clinical ECG examination. The most significant primary elements may
be represented as a color, symbol, or other easy-to-read encoding
of indicators that make the results useful and understandable for a
lay person and a medical professional. Each signal-indicator
corresponds to a single primary element. In the static mode, the
values of the indicators are preferably color-coded for a lay
person into normal, moderately or severely abnormal. This
representation constitutes a static screen. Alternatively, the
indicators may be symbol-coded, N for normal and A for abnormal
reading; or they may vibrate or produce a sound output for people
with vision or hearing impairments. For a medical professional, the
indicators provide exact, quantitative values of the primary
elements. In the dynamic mode, the indicators are preferably symbol
(or color)-coded into C for changed or U for unchanged. This
representation constitutes a dynamic screen.
Intermediate-resolution Scale II allows viewing the ECG with
automatically determined primary elements on a display and
interactive editing of the set of primary elements and their search
criteria. The editing can be performed by a user or a medical
professional to modify the set of characteristic points or to
adjust their search criteria, and can be performed either manually
or automatically by the software. The individually adjusted search
criteria can then be used to re-program the Scale I analysis as
described earlier.
Scale II allows accurate comparison of serial ECGs and detection of
small serial changes that may be unexposed by visual inspection of
the signals. This scale requires higher computational resources
than Scale I and can be implemented in a specialized processor,
computer organizer or a personal computer. These computational
resources also allow manual entering text information about the
patient into the database and specific instructions regarding
adjustment of time windows, threshold values, and other variables.
To perform the Scale II analysis, the primary elements from serial
ECGs are stored into a database to construct the time series for
each primary element. The series is decomposed into a few most
significant basis functions and coefficients using Principal
Component Analysis (PCA) or any other orthogonal set of basis
functions. The newly acquired values of the primary elements are
compared with the series of the previously obtained values.
Furthermore, the changes in the series of PCA coefficients are
analyzed to detect small cumulative changes in the dynamics of the
series that indicate instability in the cardiac electrical
activity.
High-resolution Scale III is used to analyze individual and
combined changes in the primary elements; at this scale, the number
of the primary variables is increased to include the entire
waveform of the cardiac complexes. This allows the most sensitive
and accurate detection of the small changes in the individual
electrocardiographic pattern. The same PCA approach is used at this
scale to expose small serial changes in the ECG recordings. Scale
III requires higher computational resources compared to Scale I and
Scale II; it may be implemented in a powerful processing unit such
as a personal or specialized computer or a distributed network of
computers or the Internet.
This invention can be used for one-time examinations by patients,
medical professionals, paramedics and lay public, and for dynamic
assessment of changes in cardiac electrical activity. The
information can be transmitted to an external computer system or a
network of computers. For a lay person, the system may also include
a database explaining significance of the changes in each primary
element and providing simple recommendations about the measures
that has to be taken if the readings of the indicators become
abnormal. These may include complete cessation of physical
activity, contacting a medical professional, taking a medication,
etc. More detailed recommendations might be provided for patients
who have specific abnormalities or medications. These patients
might require special monitoring or individual adjustment of their
primary elements. For example, specific monitoring the duration of
QT-interval is important in patients taking antiarrhythmic drugs
that prolong QT-interval.
The system can be used as: Hospital or medical center information
management; Information management for ambulatory patients;
Information management for community health program; Information
management for corporate health program; Self-awareness and health
advice system; Information management for patients with implantable
devices; Medical decision support system for medical professionals
implemented on a personal computer, a cell phone, a smart phone, or
a personal digital assistant (PDA); Information management or
decision support system that includes personalized analysis of
serial data and medical knowledge contained in medical literature
and on the Internet; Personalized advice system implemented on a
personal computer, a cell phone, a smart phone, or a personal
digital assistant (PDA); First-aid health-data analyzer for
emergency units, paramedics, and medical personnel; Health data
analyzer for a routine medical examination; A personal one-time or
serial data analyzer with storage of individual historic data,
adaptive adjustment of individual thresholds and assessment of
changes in individual heath pattern; A one-time or serial
health-data analyzer for a group of people, a family or a patient
group, with storage of individual historic data for each person,
adjustment of individual thresholds and assessment of changes in
individual health patterns; Event-monitoring device including
patient-detected events; Bedside monitoring; Bedside or ambulatory
monitoring providing intelligent alarms to medical professionals
when appropriate; At least one of arrhythmia, stress-test,
ischemia, ST-segment, and T-wave alternans monitoring; Pacemaker
and other implantable device checking, bi-directional or
uni-directional communication, programming, and control; Evaluation
of the treatment efficacy, side effects and progression of the
disease.
Accordingly, an object of this invention is to provide a system for
analyzing ECG signals at least at two levels of detail or
resolution. Both levels of resolution are presented in simple
representation that can be understood by lay persons, as well as
medical professionals.
A further object of this invention is to provide an ECG analyzing
system that includes a monitoring device for receiving and
analyzing ECG signals and which includes means for communicating
with an external computer to which the ECG signals can be forwarded
for more complex analysis. The monitoring device can be
reprogrammed by the external computer to select the primary
elements of the ECG signals that are unstable or abnormal. The low
level analysis performed by the monitoring device is thus focused
on the critical primary elements for that patient.
The system of the present invention can be used for management and
analysis of electronic health (medical) records and information,
analysis and management of biometric data, or information
management of other types of healthcare data.
The system of the present invention provides instant access to
information from a variety of distributed sources to reduce costs,
improve quality of patient care and optimize decision making. For
example, the system can be used to provide a real-time view of
in-hospital patient distribution and operations structure in
different departments and at different stages of the treatment
process, from admission to discharge, or in the Emergency Room. The
system can capture and integrate monitoring of vital signs,
biometrical data, capture and integrate text, images, technical
information related to device functioning and instrumentation
status. The system can also provide an intelligent, tailored
representation for different types of users and different points of
care. For example, it can improve information sharing among the
healthcare providers, including physicians, nurses, technicians,
clerks, and others. The system of the present invention can also
facilitate analysis, management, and optimization of information
processing from the traditional departmental systems--e.g., legacy
systems (Nursing, Pharmacy, LIS, RIS, PAS, by creating integrated
database, applying intelligent analysis and optimizing diagnosis
and treatment, including diagnostic and treatment plans and
providing intelligent alarms and alerts to support and optimize
clinical decision making.
The system of the present invention can collect real-time
physiological and health data from a variety of sensors including
vital sign monitors, ventilators, infusion pumps. It can also
support a wide range of physiologic sensors from a variety of
manufacturers. The system can also automatically re-configure
itself to accept and recognize new data from physiological sensors
whenever a new sensor is plugged into the system. It is also
possible to enter new data into the system using an integrated
barcode scanning or RFID tag or MEMS tag or other types of
automatic entry of information at the bedside in a real time. The
system of the present invention can also adapt, compare and merge
new information with the data that already exist in the system.
Because the information flow between different levels/units of the
system is bi-directional, the system supports and optimizes
seamless exchange of data coming from different diagnostic and
treatment modalities, such as patient information from hospital
data repositories (e.g., Laboratory, Medication,
Admission/Discharge/Transfer and others) and intelligently alert
the clinician to potential problems.
The system can also have multiple displays, terminals, including
wireless connections with personal handheld devices (PDA, Smart
Phones, Cell phones, computers, and computer tablets). Using these
displays, users can simultaneously receive different modes of
information, such as physiological signal information (vital signs,
ECG, blood pressure, cardiac output), real-time intelligent alerts,
prescription dispensing, drug interaction, dynamical report,
individual patient dynamics, and serial comparison of individual
patient's data, etc.
For example, an acute ischemic syndrome (AIS) can be confirmed by
measurements of the level of cardiac enzymes (troponins). Since the
level of enzymes can be estimated only in a hospital, this
information is usually unavailable when the subject is admitted to
the emergency room. In the absence of this information, medical
decision is made on the analysis of clinical and
electrocardiographic signs of ischemia. Yet, this information is
incomplete. Thus, the information completeness is estimated
relative to the total, theoretically possible, information about a
disease state (which is equal to 1), so that the sum of information
content (probability estimates, or uncertainty) of all diagnostic
tests is equal to 1. The information contained in each test is
equal to a number between 0 and 1. At each scale the information
completeness (probability of each disease state) can be estimated
relative to the complete information (reference) for this disease
state. Similarly, the information completeness is also estimated
for all scales, relative to the complete, theoretically possible
information in all scales.
The probability or information completeness can be represented by
the probability transition matrix of a Markov chain, Bayesian
probability, probabilistic neural network, or some other
non-probabilistic matrices and methods.
Traditionally, the term "multiscale analysis" or "multi-resolution
analysis" refers to either (1) a spatial multiscale analysis
(distributing analysis of complex structures or processes that span
different spatial scales, for example,
molecular-cellular-organ-body scales of biological processes into
several spatial scales), or (2) a temporal multiscale analysis
(distributing analysis of complex, dynamic processes that involve
several different time-scales). The term multiscale analysis used
herein refers to the temporal multiscale analysis adapted to serial
(longitudinal) data or a combination of temporal and structural
multiscale analysis adapted to serial (longitudinal) data (because
serial images, image information, and other data spanning different
spatial scales can be also included in the analysis). Note that the
traditional temporal multiscale analysis refers to an application
of a mathematical formula or function (for example, a wavelet
function or a nonlinear function, such as entropy), to different
time-scales by varying a time-window parameter (i.e. using a
mathematical translation or dilation of a function). A detailed
description of a multiscale wavelet analysis can be found in The
Statistician (2000) 49, Part 1, pp. 1-29 (Abramovich F, Bailey T C,
Sapatinas T. Wavelet analysis and its statistical applications.). A
description of a multiscale entropy analysis can be found in
Physical Review E 71, 2005, pp. 021 9061-021 9061 8 (Costa, M,
Goldberger A L, Peng, C.-K. Multiscale entropy analysis of
biological signals). In this approach, the fundamental mathematical
function remains unchanged at all time scales, but the scaling
parameters change. Our multiscale approach, presented herein and in
our previous Applications (application Ser. No. 10/816,638, filed
Apr. 2, 2004, which is a continuation-in-part of application Ser.
No. 10/124,651, filed Apr. 17, 2002, now U.S. Pat. No. 6,925,324,
which was a continuation-in-part of application Ser. No.
09/583,668, filed May 30, 2000, now U.S. Pat. No. 6,389,308),
incorporated herein by reference, is different from the traditional
methods for multiscale analysis described above (in some respects,
it can be viewed as a non-trivial generalization of the traditional
multiscale and multi-resolution approaches). It allows 1) usage of
different mathematical, pattern-recognition, statistical,
probabilistic, artificial-intelligence
functions/models/estimates/approximations at different time scales,
2) usage of a single time-point (snapshot) compared against
reference values at the 1.sup.st scale of analysis (this snapshot
analysis can be performed one-time, periodically,
quasi-periodically, or continuously) and multiple time-points
(serial data) at the higher-scales of analysis, 3) usage of
composite functions and estimates obtained by combining different
parameters and time-scales at higher-level analytical scales, 4)
bi-directional exchange of information between different scales to
improve the analysis. Using recently introduced terminology, our
multiscale analysis approach can also be viewed as a non-trivial
extension, improvement, and generalization of a recursive
projection method (Shroff G M, Keller H B. (1993) SIAM J. Numer.
Anal. 30, 1099-1120) that can be also adapted for "equation-free
modeling" (Theodoropoulos C, Qian Y-H, Kevrekidis I G. PNAS (2000)
97, 9840-9843) of multiscale, complex processes.
The above and other objects and advantages of this invention will
be more fully understood and appreciated by reference to the
following description and the drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
A full understanding of the invention can be gained from the
following description of the preferred embodiments when read in
conjunction with the accompanying drawings in which:
FIG. 1 is a block diagram of the multi-scale (multi-resolution,
multi-level, multi-layer) method and system of the preferred
embodiment of this invention.
FIG. 2 is a block diagram of the analysis unit of a physiological
monitoring system (for example, an electrocardiographic, ECG
system), which is interfaced with the first level of the system of
the present invention (to incorporate the ECG data, processed or
unprocessed), as shown in FIG. 1, bottom level.
FIG. 3 shows the set of indicators that represent the results of
ECG analysis at Scale I both qualitatively and quantitatively in a
static mode ("N" denotes normal value and "A" denotes an abnormal
value of a characteristic parameter).
FIG. 4 shows the set of output indicators that represent the
results of ECG analysis at Scale I both qualitatively and
quantitatively in a dynamic mode ("U" represents unchanged value
and "C" represents a changed value of a characteristic parameter
compared to a previous recording).
FIG. 5 is a flowchart of operation of the preferred embodiment.
FIG. 6 is a graph of a representative electrocardiogram from a
normal subject and its segmentation into a plurality of
characteristic points and segments.
FIG. 7 is a graph of a representative electrocardiogram from a
patient with a cardiac disease, large Q-wave, and prolonged
QT-interval (0.5 sec) compared to the normal ECG shown in FIG.
6.
FIG. 8 shows the readings from the output indicators at Scale I in
the static mode for the abnormal ECG in FIG. 6 (N denotes a normal
value, A denotes an abnormal value of a characteristic parameter
compared to default values).
FIG. 9 is a graph of ECG obtained from the same patient as in FIG.
8 several hours later. The amplitude of T-wave decreased by 0.3 mV
compared to the previous recording shown in FIG. 7.
FIG. 10 shows the readings from the indicators at Scale I in the
dynamic mode for the abnormal ECG in FIG. 9.
FIG. 11 shows the time series of QT-intervals (panel A) and its
first three PCA-coefficients (panels B-D) in patient A during one
month.
FIG. 12 shows the time series of ST-segment and T-wave amplitudes
(panel A) and its first three PCA-coefficients (panels B-D) in
patient A during one month.
FIG. 13 shows serial ECG tracings of patient A during one
month.
FIG. 14 is a plot of the first PCA-coefficient obtained from the
series of QT-intervals versus the first PCA-coefficient obtained
from the series of T-wave amplitudes in patient A.
FIG. 15 is a flow chart of multi-scale analysis and representation
of health data in accordance with this invention.
FIG. 16 is a flow chart showing horizontally and vertically
distributed multiscale analysis and representation of health data
in Levels I and II (with horizontally distributed Level I
analysis).
FIG. 17 is a flow chart showing horizontally and vertically
distributed multiscale analysis and representation of health data
in Levels I and II (with horizontally distributed Level I and II
analyses).
DESCRIPTION OF THE PREFERRED EMBODIMENTS
FIG. 1 is a block-diagram of a preferred embodiment of a system for
at least one of information management, decision support,
diagnosis, examination (physical, physiological, biochemical,
etc.), monitoring, advice, medical recommendation, and
bi-directional communication between individuals (patients),
medical professionals (physicians, nurses, technicians) and medical
centers. The system may receive physiological or health data (for
example, ECG data) from a recorded data source for analysis, but
preferably receives the data real-time, on-line. As used herein,
patient means an animal, and most likely a human. The medical
device further includes an analysis unit or module 40 which, in
turn, consists of processing, compression, storage, and comparison
units (FIG. 2). The processing unit 41 can be a typical computer or
personal computer of the type available from many vendors such as
IBM and Hewlett-Packard. The processing unit 41 is programmed to
detect a plurality of characteristic points such as the onset, peak
and offset of P-, Q-, R-, S-, T-, U-waves, and computes the
characteristic parameters or primary elements which include
amplitudes of the said waves and ST-segment, duration of PQ-, QRS-,
and QT-intervals. The processing unit 41 has a programmable
microprocessor that can be programmed to modify or change the set
of primary elements or to adjust their search criteria. This allows
individual adjustment of the characteristic points which, in turn,
increases the accuracy of detection of the primary elements. For
instance, in signals with biphasic T-wave, two T-peaks should be
detected, whereas monophasic T-wave requires detection of a single
T-peak. Furthermore, the criteria for determining the offset of
biphasic T-wave are different from the criteria for the offset of
monophasic T-wave. Individual adjustment of the primary elements
and their search criteria increases the accuracy of the detection
of characteristic points in different ECG patterns. Still another
possibility is analysis of combined changes in some primary
elements or disabling analysis of the other elements. For example,
in patients with possible electrolyte abnormalities, the amplitudes
of the T-wave and U-wave may be combined into a single index which
will be convenient for monitoring. Furthermore, the set of
monitored primary elements can be modified according to the
specifics of cardiovascular abnormality. For example, in patients
with coronary artery disease, the amplitude and the slope of the
ST-segment should be monitored continuously.
Compression unit 42 compresses the ECG waveform into a few weighted
basis vectors and their coefficients using principal component
analysis, wavelet decomposition, or other orthogonal mathematical
transformation. Storage unit 43 stores the compressed waveforms and
the computed primary elements into memory. Comparative unit 44
compares the newly acquired waveforms and newly computed primary
elements with the waveforms and primary elements previously stored
in the storage unit 43. The analysis unit 40 has means for
adjusting the thresholds for each indicator, whereas the default
values correspond to normal ECG. An output unit 60 includes a
screen or a set of indicators for displaying the ECG waveforms and
the computed primary elements in comparison with the previously
stored primary elements or in comparison with the default reference
values. The results of comparison can be represented both
qualitatively and quantitatively in the dynamic and static modes.
In the static mode, the quantitative representation includes exact
values of the primary elements and the type of the cardiac
complexes, whereas the qualitative representation includes
indication of each parameter as being normal (N) or abnormal (A) as
shown in FIG. 3. Abnormal readings may be further classified into
moderately abnormal and severely abnormal. To make the indicators
understandable to a lay person, the degree of abnormality may be
color-coded: green color corresponds to a normal value, yellow
corresponds to a moderate abnormality, and red corresponds to a
severe abnormality. In the dynamic mode, the quantitative
representation shows the differences between the newly acquired and
stored primary elements and waveforms, whereas the qualitative
representation includes indication of each parameter as being
changed (C) or unchanged (U) as shown in FIG. 4. The output unit 60
may alternatively or additionally feed an output data to an action
unit 80 for sounding an alarm, generating a vibration, or taking
appropriate measures, such as applying the drugs or adjusting the
therapy mode. Communication unit 100 transmits the information
between the device 10 and external higher-level processing device
150. The communication unit 100 may be a modem or a wireless
transmitter/receiver. Electrocardiographic signals and recorded
values of primary elements and indexes are transmitted from the
device 10 to higher level devices for more detailed processing and
storage. The higher-level device 110 preferably transmits back to
device 10 a set of primary elements and their search criteria to be
used in device 10.
FIG. 5 is a flow-chart of operation of this medical device.
FIG. 6 shows a representative ECG obtained from a normal subject
and position of the characteristic points in the signal.
To achieve the optimal sensitivity in the detection of hidden or
small ECG changes, a pattern recognition approach is used that
extracts the basis functions from the statistics of the signal
itself and gives the least error representation of the signal.
Specifically, a principal component analysis (PCA) is applied which
requires a minimum number of basis functions to obtain a fixed
reconstruction error compared to other orthogonal expansions.
PCA is an orthogonal transformation that employs a weighted
combination of several basis functions to represent a signal. The
basis functions are fixed, whereas PCA-coefficients vary as a
function of time. The choice of PCA for detection and
characterization of the changes in ECG-signal was related to the
following properties of the transform: minimization of the mean
square error within a finite number of basis functions guarantees
that no other expansion will give a lower approximation error (with
respect to the mean square error). clustering transformational
properties with minimization of the entropy in terms of the average
squared coefficients used in the expansion.
In contrast to the methods that use fixed-form basis functions (for
example, Fourier representation), basis functions in PCA are
derived from the statistics of the signal. Therefore, PCA with the
same number of basis functions provides a smaller residual error
than other expansions.
Assume that the pattern contains M vectors x.sub.i i=1, 2, . . . ,
M, and the length of each vector is equal to N points. To obtain
the PCA coefficients, the matrix C.sub.x must be obtained using the
average of the covariance matrices of x vectors. The matrix C.sub.x
is defined as C.sub.x=E{(x-m.sub.x)(x-m.sub.x).sup.T} (1) where
m.sub.x=E {x} (2) is the mean vector, and E corresponds to the
expected value. Assume that the pattern of the time series has M
unit-length vectors x.sub.i, i=1, 2, . . . , M, and the length of
each vector is equal to N points, to generate a matrix C.sub.x from
the outer products of vectors x. A matrix C.sub.x of M vectors
x.sub.i can be calculated as
.apprxeq..times..times..times..times..times..times..times..times..apprxeq-
..times..times..times. ##EQU00001##
From the matrix C.sub.x one can obtain eigenvectors .psi..sub.i
i=1, 2, . . . , N and corresponding eigenvalues .lamda..sub.i i=1,
2, . . . , N. Let A be the transformation matrix whose rows are the
eigenvectors of C.sub.x. First eigenvector corresponds to the first
eigenvalue, second one corresponds to the second eigenvalue and so
on. Eigenvalues are arranged in decreasing order so that
.lamda..sub.1.gtoreq..lamda..sub.2.gtoreq. . . .
.gtoreq..lamda..sub.N. Then, PCA consists of a multiplication of
the transformation matrix A by vector (x-m.sub.x): y=A(x-m.sub.x)
(5) where y is a PCA coefficient vector. If matrix A is formed by K
eigenvectors that correspond to the largest eigenvalues, y is a
K.times.1 vector. Then, the first K coefficients contain almost
entire information about the signal allowing substantial reduction
in the number of analyzed coefficients and thus compression of the
data. In this application, PCA is applied to the time series of
each primary element, that is the intervals between the cardiac
beats, duration of PQ, QRS, and QT-intervals, amplitudes of P-, Q-,
R-, S-, and T-waves. For instance, to determine the characteristic
pattern of the series of QT-intervals from the serial ECGs, assume
that the pattern consists of M unit-length vectors x.sub.i.
Therefore, the series is divided into M constant-length time
windows to obtain vectors x.sub.i. Alternatively, the unit-length
vectors x, may be comprised of a combination of all or some primary
elements to determine a typical combinatorial pattern of the
primary elements. Still another possibility is an extension of the
concept of the unit-length vectors x, into two dimensions to
represent both the combined pattern of all primary elements (in the
first dimension) and the serial changes of each primary element (in
the second dimension). Then PCA analysis is performed as described
above. Applications of Mathematical Transformations at Scale II and
Scale III of the System
The analysis described hereafter could be used as a stand-alone
tool or a part of an integrated processing and analytical system,
such as an artificial intelligence system, which includes neural
networks and expert systems. The analysis could be performed on a
single computer or a distributed computer network, possibly, with
parallel processing. In previous works, mathematical
transformations, and in particular, the principal component
analysis (PCA) was applied for detection and classification of
cardiac waveforms (QRS-complexes and ST-segments) in ECG. The
optimal basis functions for QRS or ST waveforms were obtained from
large training sets. PCA coefficients were used to compare
individual waveforms with the set of templates and to assign the
waveform to one of the classes.
Instead of applying PCA to the signal as in the previous art
studies, this invention preferably applies PCA to the time series
of primary elements that are extracted from the ECG-signal. This
modification provides the following advantages. First, this
provides an objective and accurate estimation of the serial changes
in the ECG-signals and reveals small or hidden abnormalities that
cannot be exposed by the previously used techniques. Second, this
allows dramatic compression of the data. Third, this analysis
reveals independent changes in each primary element when
simultaneous changes occur in several elements. The prior art
analysis of the original ECG signal might not show any changes
because of the cancellation effects between the elements undergoing
changes in opposite directions.
Because the time series of primary elements is nonstationary and
highly variable among subjects and in the same subject over
different periods of time, typical waveforms or templates of this
series cannot be determined. Therefore, temporal, adaptive changes
in PCA coefficients are used to detect and characterize the changes
in this series. Pronounced and complex changes in the series of
primary elements are identified by the simultaneous changes in
several PCA coefficients. Since the basis functions in this
expansion are orthogonal, simultaneous changes in several
coefficients represent complex disturbances in linearly independent
components of the signal. These combined changes in PCA
coefficients reveal serious instabilities in the cardiac function
as shown in the following examples.
The signal is separated into consecutive windows, and an array of
vectors is obtained from the series. A covariance matrix is formed
by the formula (3), where M is the number of vectors, x.sub.i is
i.sup.th vector, and m.sub.x is calculated as in formula (4). Basis
functions or eigenvectors are obtained from this matrix. Since only
one covariance N.times.N matrix (N is the window length) is
generated from the signal, all eigenvectors are fixed.
Example I
The following example illustrates the sequence of ECG analysis at
the system's Scales I, II and III. Serial ECG recordings from a
patient A who had a structural heart disease and dynamic changes in
the electrocardiogram were processed at each Scale with a different
degree of detail. Scale I revealed the changes in a small number of
important, primary elements using minimum computational resources.
Scale II exposed changes in the primary elements that occurred in
serial recordings over time. Scale III provided complete
description of the serial ECG changes using a complete set of
primary elements and their combinations.
System Initialization.
When the system is used for the first time, initialization is
required for verification and individual adjustment of the analysis
criteria including identification of the primary elements and their
search criteria. System initialization is performed using the
hardware and software resources of the intermediate resolution
Scale II and high resolution Scale III. In the initialization mode,
the Scale I device transmits ECG to the higher Scale of the system
via a direct or a wireless (telemetry or infrared) link. The ECG
and the position of primary elements and their characteristic
points (onset, peak, and offset) are visualized on a display, for
example LCD display, as shown in FIG. 6. The position of
characteristic points can be verified and manually edited by a
user, a lay person or a medical professional. A simple manual or a
software tutoring program of the typical ECG patterns, the primary
elements and their characteristic points is provided for a lay
person. FIG. 7 shows an ECG with a long QT-interval (0.5 sec) and a
low-amplitude T-wave compared to the normal ECG shown in FIG. 6.
The offset of this low-amplitude T-wave is difficult to detect
automatically and a manual verification and correction are desired
to ensure the accuracy. A user may also modify the set of monitored
primary elements to account for a specific cardiovascular
abnormality. Some of the elements may be combined into a single
monitoring index, for example, a combined integral of T and U peaks
can be useful for patients with possible electrolyte
abnormalities.
After finishing manual verification and editing, the system
automatically adjusts the search criteria for each characteristic
point which include the time window, the amplitude, integral and
derivative thresholds. The individually adjusted program is
generated for a particular person and is automatically sent to
re-program the processing sub-unit of Scale I. After the
initialization, the Scale I device can work in autonomous regime
without permanent connection to the higher-level Scales.
Re-initialization and serial adjustment can be performed to modify
the set of primary elements and indexes and their search criteria.
In addition to the procedure that was described in the system
initialization, the results of the Scale II analysis can be used
for serial adjustment. In particular, the primary elements and
indexes whose time series and PCA coefficients demonstrate unstable
behavior can be identified and included into the Scale I
analysis.
Scale I. FIG. 7 is a graph of a representative electrocardiogram
which has large Q-wave, and prolonged QT-interval. These
abnormalities have been detected by the method of the present
invention at the Scale I and represented qualitatively as abnormal
findings and quantitatively as the exact magnitude of changes
compared to the default values as shown in FIG. 8 which are
readings of output indicators at Scale I for abnormal (A) and
normal (N) ECG in the static mode. FIG. 9 is a graph of ECG
obtained from the same patient several hours later. The amplitude
of T-wave decreased by 0.3 mV compared to the previous recording
shown in FIG. 8. The amplitude of T-wave decreased by 0.3 mV
compared to the previous recording shown in FIG. 7. FIG. 9 shows
the readings from the output indicators that represent the changes
(C) in this ECG compared to the previous one.
Scale II. Serial ECGs have been obtained from patient A. and
processed by means of Scale II to expose the time course of the
serial changes that occurred in the this patient over a period of 1
month. FIG. 11, panel a, represents the series of QT-intervals that
were extracted from these recordings; panels b-d demonstrate the
changes in the first three PCA-coefficients that were obtained from
this signal. At the end of the last recording, the patient
developed a life-threatening disorder of cardiac function. However,
this method reveals instability in the cardiac function as early as
20 days before the event when all known physiological indicators
remain normal. FIG. 12 demonstrates changes in the ST-segment and
T-wave amplitude extracted from the same recordings (panel a) and
the corresponding first three PCA-coefficients. The time series are
complex and the changes cannot be easily described or analyzed by
simple tools, therefore, the changes in the signal are analyzed in
a compressed form using the series of the first three
PCA-coefficients which contain the most significant information
about the signal. The ECG was relatively stable during the first 10
days but then became unstable as reflected by variations in the
PCA-coefficients. The patient suffered a life-threatening cardiac
disorder at the end of the month. However, variations in the
PCA-coefficients were observed long before the event, when all
physiological indicators remained normal. Calculating the changes
in the variance of the PCA coefficients provides an accurate
estimation of the changes and stability of the series. Unlike
linear estimators such as the mean and variance of the signal or
nonlinear estimators such as fractal scaling exponent or
correlation dimension, disturbances in the PCA coefficients are
indicative of any changes in the pattern of the signal. Therefore,
analysis of PCA coefficients reveals both linear and nonlinear
changes in the signal.
Scale III. The same ECGs that were analyzed at the Scales I and II,
were further processed by means of Scale III to expose the entire
dynamics of the ECG signal. FIG. 13 demonstrates the ECG waveforms
that were obtained from serial ECG recordings in patient A. Since
all the data points are included into the analysis, the changes in
the shape and polarity of T-wave can be easily detected in the
serial ECGs using visual inspection, PCA or other signal processing
tools. The polarity of the T-waves are negative in days 2 and 10
recordings, and are positive in days 6, 16 and 25 recordings.
FIG. 14 shows the changes in the PCA coefficients of these series
in Scale III, dynamics of ECG in patient A in a space of the first,
most significant PCA-coefficients. Y-axis represents the first
PCA-coefficient that was obtained from T-wave amplitude. X-axis
represents the first PCA-coefficient that was obtained from
QT-interval. Each point corresponds to one-hour value. Values
during 1-5 days are marked as pluses, values during 6-10 days are
marked by stars, values during 11-16 days are marked by circles.
Higher dispersion and change in the location of the points during
6-16 days compared to the first five days indicates instability of
serial ECGs. A small cluster of data points in the lower right
corner of the figure corresponds to the unchanged signals during
the first 5 days of the recording. Then, the dispersion of the
points increases and their location changes which reflects
increased instability of the signals. Thus, the combined changes in
the coefficients that were obtained from different primary elements
revealed instability in the cardiac activity that preceded
aggravation of the cardiac disease.
It is therefore seen that this invention provides an ECG analysis
system and method for detecting a plurality of primary elements in
an ECG signal, and comparing the detected signals with reference
values both quantitatively and qualitatively. The outputs from the
system in both low level resolution and higher levels of resolution
can be understood by both lay persons and medical professionals.
The system includes means for exchanging information and direction
from an external computer for analysis and modification of the low
resolution analysis of the signal.
The information exchange (including at least one of raw data such
as electrocardiographic signals and results of analysis, such as
primary elements derived from the electrocardiographic signal) from
the lower scales to higher scales and back from the higher scales
to the lower scales can be continuous or intermittent. For example,
the 1.sup.st lower-resolution scale can be implemented in a
specialized, small-size sensor with a microprocessor, which
continuously transmits data to the 2.sup.nd scale, which can be
implemented in a cell phone, a smart phone, a PDA, a computer, or a
specialized processor. Thus, the 1.sup.st scale sensor can transmit
the data to the cell phone/PDA/smart phone/computer (2.sup.nd scale
device) continuously or intermittently, when some changes in
primary elements are identified. The 2.sup.nd scale device can also
transmit the data to the 3.sup.rd scale, local or remote device
(server) via a cell phone, Internet or other communication channels
continuously or intermittently, when its analysis detects certain
changes or when directed by a user. The data transmission between
the 1.sup.st and 2.sup.nd scales and between the 2.sup.st and the
3.sup.nd scales can be done simultaneously or asynchronously.
Alternatively, the 1.sup.st scale analysis can be implemented in a
cell phone/PDA/smart phone/computer (1.sup.st level device), so
that the sensor transmits the data to the 1.sup.st level device
continuously or intermittently (as programmed or directed by a
user). The 1.sup.st level device can also simultaneously display at
least some of the received signals (in a real time or with a delay)
and/or transmit them further to a remote or local 2.sup.nd level
device via a cell phone, internet or other communication channel
continuously or intermittently, when certain changes are detected
(for example, in some primary elements) compared to a baseline or
pre-defined thresholds, or as directed by a user or by software
settings (for example, once an hour).
Example II
The following example illustrates the sequence of ECG analysis at
the system's Scales I, II and III. Serial ECG recordings from a
patient A, who had a structural heart disease and dynamic changes
in the electrocardiogram were processed at each Scale with a
different degree of detail. Scale I revealed the changes in a small
number of important, primary elements using minimum computational
resources. Scale H exposed changes in the primary elements that
occurred in serial recordings over time. Scale III provided
complete description of the serial ECG changes using a complete set
of primary elements and their combinations.
System initialization. When the system is used for the first time,
initialization is required for verification and individual
adjustment of the analysis criteria including identification of the
primary elements and their search criteria. System initialization
is performed using the hardware and software resources of the
intermediate resolution Scale II and high resolution Scale III. In
the initialization mode, the Scale I device transmits ECG to the
higher Scale of the system via a direct or a wireless (telemetry or
infrared) link. The ECG and the position of primary elements and
their characteristic points (onset, peak, and offset) are
visualized on a display, for example LCD display, as shown in FIG.
6. The position of characteristic points can be verified and
manually edited by a user, a lay person or a medical professional.
A simple manual or a software tutoring program of the typical ECG
patterns, the primary elements and their characteristic points is
provided for a lay person. FIG. 7 shows an ECG with a long
QT-interval (0.5 sec) and a low-amplitude T-wave compared to the
normal ECG shown in FIG. 6. The offset of this low-amplitude T-wave
is difficult to detect automatically and a manual verification and
correction are desired to ensure the accuracy. A user may also
modify the set of monitored primary elements to account for a
specific cardiovascular abnormality. Some of the elements may be
combined into a single monitoring index, for example, a combined
integral of T and U peaks can be useful for patients with possible
electrolyte abnormalities.
After finishing manual verification and editing, the system
automatically adjusts the search criteria for each characteristic
point which include the time window, the amplitude, integral and
derivative thresholds. The individually adjusted program is
generated for a particular person and is automatically sent to
re-program the processing sub-unit of Scale I. After the
initialization, the Scale I device can work in autonomous regime
without permanent connection to the higher-level Scales.
Re-initialization and serial adjustment can be performed to modify
the set of primary elements and indexes and their search criteria.
In addition to the procedure that was described in the system
initialization, the results of the Scale II analysis can be used
for serial adjustment. In particular, the primary elements and
indexes whose time series and PCA coefficients demonstrate unstable
behavior can be identified and included into the Scale I
analysis.
Scale I. FIG. 7 is a graph of a representative electrocardiogram
which has large Q-wave, and prolonged QT-interval. These
abnormalities have been detected by the method of the present
invention at the Scale I and represented qualitatively as abnormal
findings and quantitatively as the exact magnitude of changes
compared to the default values as shown in FIG. 8 which are
readings of output indicators at Scale I for abnormal (A) and
normal (N) ECG in the static mode. FIG. 9 is a graph of ECG
obtained from the same patient several hours later. The amplitude
of T-wave decreased by 0.3 mV compared to the previous recording
shown in FIG. 8. The amplitude of T-wave decreased by 0.3 mV
compared to the previous recording shown in FIG. 7. FIG. 9 shows
the readings from the output indicators that represent the changes
(C) in this ECG compared to the previous one.
Scale II. Serial ECGs have been obtained from patient A. and
processed by means of Scale II to expose the time course of the
serial changes that occurred in the this patient over a period of 1
month. FIG. 11, panel a, represents the series of QT-intervals that
were extracted from these recordings; panels b-d demonstrate the
changes in the first three PCA-coefficients that were obtained from
this signal. At the end of the last recording, the patient
developed a life-threatening disorder of cardiac function. However,
this method reveals instability in the cardiac function as early as
20 days before the event when all known physiological indicators
remain normal. FIG. 12 demonstrates changes in the T-wave amplitude
extracted from the same recordings (panel a) and the corresponding
first three PCA-coefficients. The time series are complex and the
changes cannot be easily described or analyzed by simple tools,
therefore, the changes in the signal are analyzed in a compressed
form using the series of the first three PCA-coefficients which
contain the most significant information about the signal. The ECG
was relatively stable during the first 10 days but then became
unstable as reflected by variations in the PCA-coefficients. The
patient suffered a life-threatening cardiac disorder at the end of
the month. However, variations in the PCA-coefficients were
observed long before the event, when all physiological indicators
remained normal. Calculating the changes in the variance of the PCA
coefficients provides an accurate estimation of the changes and
stability of the series. Unlike linear estimators such as the mean
and variance of the signal or nonlinear estimators such as fractal
scaling exponent or correlation dimension, disturbances in the PCA
coefficients are indicative of any changes in the pattern of the
signal. Therefore, analysis of PCA coefficients reveals both linear
and nonlinear changes in the signal.
Scale III. The same ECGs that were analyzed at the Scales I and II,
were further processed by means of Scale III to expose the entire
dynamics of the ECG signal. FIG. 13 demonstrates the ECG waveforms
that were obtained from serial ECG recordings in patient A. Since
all the data points are included into the analysis, the changes in
the shape and polarity of T-wave can be easily detected in the
serial ECGs using visual inspection, PCA or other signal processing
tools. The polarity of the T-waves are negative in days 2 and 10
recordings, and are positive in days 6, 16 and 25 recordings.
FIG. 14 shows the changes in the PCA coefficients of these series
in Scale III, dynamics of ECG in patient A in a space of the first,
most significant PCA-coefficients. Y-axis represents the first
PCA-coefficient that was obtained from T-wave amplitude. X-axis
represents the first PCA-coefficient that was obtained from
QT-interval. Each point corresponds to one-hour value. Values
during 1-5 days are marked as pluses, values during 6-10 days are
marked by stars, values during 11-16 days are marked by circles.
Higher dispersion and change in the location of the points during
6-16 days compared to the first five days indicates instability of
serial ECGs. A small cluster of data points in the lower right
corner of the figure corresponds to the unchanged signals during
the first 5 days of the recording. Then, the dispersion of the
points increases and their location changes which reflects
increased instability of the signals. Thus, the combined changes in
the coefficients that were obtained from different primary elements
revealed instability in the cardiac activity that preceded
aggravation of the cardiac disease.
Example III
This theoretical example has been selected to show how the present
invention could be implemented using a distributed network of
computers with parallel processing and how it can be efficiently
integrated with such methods of artificial intelligence as neural
networks and expert systems to process different types of serial
information obtained from a patient with chronic congestive heart
failure. Patients with chronic illnesses often have a number of
chronically or intermittently abnormal indicators, whose dynamics
are difficult to discern. A network of computers allows fast and
accurate processing of the patient's information obtained using
different diagnostic techniques (such as biochemical,
electrocardiographic, nuclear magnetic resonance, stress-test, and
other modalities).
In a hypothetical patient B. with chronic congestive heart failure
(Class II) and a three-year-old myocardial infarction, the
above-described high-resolution analysis of serial ECG recordings
could reveal a subtle decreasing trend in the amplitude of the
ST-segment. This trend could be revealed because the serial ECG
recordings were processed at the high-resolution level using a
radial basis function (RBF) neural network, which was previously
trained on patient's B. electrocardiographic data. Because the
neural network could learn the typical patient's B. ECG pattern, it
could detect subtle changes in this pattern. The magnitude of the
changes may be so small and the changes so gradual, that they might
escape detection by the standard ECG processing techniques, which
are manually applied by the physicians or used by the current
commercial ECG scanning software. The computer server, where ECG
recordings from this and other patients would be stored and
analyzed, would be a part of a computer network that also includes
servers for analysis of biochemical, stress-test, nuclear magnetic
resonance, and other data. The servers would be organized into a
hybrid artificial intelligence system, which combines a neural
network and expert systems. In this system, the neural networks are
used where the rules of analysis can be modeled by a multi-node
network structure, in which each node is assigned the specific
input and output rules and connections to other nodes. On the other
hand, expert systems are used when the decision making process due
to numerous uncertainties is better represented by informal
(heuristic) rules.
The above-described decreasing ST-amplitude trend in the serial ECG
recordings lead to an activation of an expert system's rule that
initiates query of other computer servers on the network that
contain biochemical, stress-test, and nuclear-magnetic resonance
date for the same patient. After that, the server that contained
biochemical data initiates neural network analysis of the patient's
enzyme level concentration for the period of time, in which ECG
changes occurred. A small increasing trend is detected in the
cardiac myoglobin levels, and this biochemical and ECG information
are transmitted wirelessly to the personal digital assistant of an
attending physician with a suggestion of a slowly developing
ischemic process. The timely notification allows the physician to
initiate early anti-ischemic treatment and prevent potentially
life-threatening complications of the disease.
Example IV
This theoretical example is provided to show implementation of the
present invention on a specialized computer network, which could be
setup for individuals working in the high-demand professional
environments, such as airplane pilots.
During a late-spring commercial flight, a hypothetical 46-year-old
pilot suddenly developed dizziness and shortness of breath. A Scale
I ECG examination showed sinus tachycardia (fast heart rates) and
increased amplitude of the P-wave. The Scale I analysis is
performed using a portable ECG acquisition unit, which transmitted
the information wirelessly (using a Bluetooth radiofrequency
communication technology to an integrated airplane health network
(implemented using Wi-Fi wireless technology). A second
Scale-I-device (also connected to the network) is used to examine
changes in blood pressure and detected moderate increase in
diastolic pressure.
The airplane integrated health system, which includes a diagnostic
expert system, queries wirelessly the home network computer server
of the pilot (using GPS wireless communication technology) to
obtain the health data for the previous month. The home network
server, in turn, activates Scale II serial analysis of all
available health data and detects subtle but gradually increasing
instabilities in heart rate and P-wave amplitude during the
previous 3 days aggravated by physical exercises. In the health
data file, the system also identifies information regarding the
pilot's history of allergic reactions during the spring vegetation
periods. This information is transmitted back to the airplane
expert system, which combines the information and suggested an
allergic bronchial spasm. This information is transmitted
wirelessly to the personal digital assistant of an attending
physician, who from his home network system sends back a
recommendation of anti-allergic medication, which eliminates the
symptoms.
Note that the multi-scale distributed system could be configured to
operate in several different modes. In the first mode, which is
activated in the airplane, the portable ECG acquisition and
Scale-I-analysis unit transmits the data wirelessly to the
integrated airplane health network for higher-resolution analysis.
In the second mode, which is activated in a car, the portable
Scale-I-analysis unit communicates wirelessly with the car computer
network using a bluetooth technology. In the third mode (which is
activated at home), the portable ECG acquisition and
Scale-I-analysis unit transmits the data wirelessly to the home
integrated computer health network (organized using Wi-Fi
communication). In the fourth configuration (which is usually
activated outside home, on vacations, etc.), the portable ECG
acquisition and Scale-I-analysis unit transmits the data wirelessly
to the personal digital assistant (PDA) or a cell phone or a smart
phone (a combination of a cell phone and a PDA) for Scale II
analysis. If needed, this Scale-II-analysis unit then connects
wirelessly (using a cell phone GSM communication technology) to a
home health computer network. Alternatively, this fourth mode of
operation (with a PDA or a cell phone for Scale II analysis) could
be selected to operate at home, in a car, in the airplane, and in
other settings.
Example V
This theoretical example is selected to show application of the
present invention for tracking dynamics of health data in patients
with implantable cardiac devices.
A hypothetical patient with an implantable
cardioverter-defibrillat- or has developed subtle instabilities of
cardiac rhythm and slowly rising average heart rate. These changes
are detected by the implantable device, which transmits this
information wirelessly to a home health network computer. The
network computer performs serial analysis of the recordings at
Scale III resolution. At the same time, the computer reaches a
hospital network server and queries the recordings from the same
patient during his recent hospitalization. Inclusion of these
recordings into the Scale III analysis shows that a similar
instability of heart rate was observed in this patient only prior
to onset of life-threatening cardiac arrhythmia. Another personal
device (also connected to the network) for tracking changes in
blood pressure shows instability of blood pressure. An artificial
intelligence system (which was integrated with the Scale III
analysis) is automatically activated to interpret these findings.
The system assesses the findings as clinically significant and
forwards them wirelessly to a personal digital assistant of an
attending physician, who decides to initiate preventive
beta-blocking therapy. During the next six hours of monitoring, the
Scale II and Scale III analysis shows stabilization of cardiac
rhythm.
Example VI
This theoretical example describes potential benefits of the
present invention in patients with congestive heart failure
undergoing bi-ventricular resynchronization pacing therapy (using
the implanted bi-ventricular pacing device, such as a Medtronic
Insync Marquis III.TM. device or a Medtronic Optivol).
A hypothetical patient with chronic congestive heart failure
undergoing resynchronization pacing for 15 months has developed a
gradual increase in the intrathoracic impedance, detected by
Optivol, indicative of slowly progressing decompensation of cardiac
function. These changes are detected by the implanted device, which
used individually tailored monitoring thresholds at the Scale I
analysis. The thresholds were adjusted using the individual
patient's reference values determined at the Scale II-II analysis
(which was performed on a hospital health network). The changes in
the intrathoracic impedance detected by the implanted device with
Optivol are transmitted wirelessly to the hospital computer network
for higher-resolution, in-depth processing. The Scale II-III
analysis confirms that the magnitude of the changes exceeded 3
standard deviations never been observed in this patient previously.
The information is transferred to the integrated artificial
intelligence system for further interpretation. The system
classifies the changes as clinically significant and forwarded them
to the medical personnel. Considering these changes, a decision is
made to hospitalize the patient for detailed examination and
therapy adjustment.
A similar hypothetical example can be envisioned for a patient with
an implantable device having several sensors, such as the
intrathoracic impedance sensor and the intra-atrial pressure
sensor. The analyses at the 1.sup.st and 2.sup.nd scales for each
sensor's data would be performed as described above. However, at
scale III analysis, the data obtained from both sensors will be
integrated to obtain an integrated, personalized, dynamical
profile. If the trends of data obtained from both sensors are
consistent with progressive deterioration of cardiac function, this
would increase the probability of a dynamical diagnosis of a heart
failure worsening. However, if the trends of data obtained by both
sensors are contradictory, the probability of a diagnosis would
decrease.
A hidden Markov model (HMM) could be used to track the dynamic
probability of a change in the state of a disease (for example,
heart failure worsening). For example, a left-right HMM can be
constructed in such a way that the probability of a change in the
disease (heart failure) state is associate with a set of parameters
.THETA.={.pi., A, B}, where .pi. is a prior probability (i.e. the
probability that the subject is initially at a certain state of
heart failure), A is a transition matrix of probabilities of going
from one state of the disease (heart failure) to another, and B is
a matrix of emission probabilities that describe the likelihood of
a certain symptom or a diagnostic indicator (primary element) or a
change (dynamics) in the properties of the primary elements when
the subject's disease (heart failure) is in a certain state (for
example, the heart failure NY state III). If the symptoms (or
primary elements) are continuous, then the matrix B contains
functions, probability density functions, vectors, or mixtures of
functions. The above matrices can also contain some
parameterizations derived from the data, such as mean/median values
and variances or ranges.
The joint likelihood of a sequence of serial changes in data or its
primary elements (i.e. the likelihood that an observed sequence of
dynamic changes in data or primary elements occurs when a certain
sequence or path of changes in the disease (heart failure) status,
Q, occurs) can be computed using Bayes conditional probability:
P(X,Q|.THETA.)=P(X|Q,.THETA.)P(Q|.THETA.) (6)
.times..times..times..THETA..times..times..times..THETA..times..times.
##EQU00002##
Formula (7) represents the likelihood of a certain sequence of
changes in primary elements, X, along a certain path of changes in
the disease (heart failure) states, Q. Next, probability of a
sequence of disease (heart failure) states given a set of
parameters .THETA. is equal to a product of transition
probabilities along this path.
.times..THETA..pi..times..times..pi..times..times. ##EQU00003##
Using formulas (6)-(8), one can compute the likelihood of an
observed sequence of dynamic changes in data or primary elements
for each sequence or path of changes in the disease (heart failure)
state, Q. Then, one can determine the sequence or path of changes
in the disease (heart failure) state associated with the greatest
likelihood, and this will be the most likely sequence of changes in
the disease state. To reduce the amount of computations, this
analysis can be implemented in software using a recursive Viterbi
algorithm, or other recursive, forward-backward computer
algorithms. To represent (visualize) the dynamic changes in the
probabilities of different diseases (or disease states) for a user,
one can use Trellis diagrams.
To determine the most probable HMM for a given sequence of serial
changes in the primary elements or data, one can compute first, for
each HMM, the probability of the most likely path (sequence) of
changes in the disease state as described above (see (6)-(8)).
Then, an HMM that has the greatest probability of the most likely
path would be the most probable HMM model.
One can also compute a normalized probability of an HMM, by
normalizing the probability of the most likely path (sequence) of
changes in the disease state for each HMM, by a sum of the joint
likelihoods for the particular sequence of serial changes in
primary elements and all possible disease (heart failure) states,
Q, allowed by the HMM as follows:
.times..THETA..times..times..times..THETA. ##EQU00004##
Equation (9) also provides a way to compute the probability of
observing a certain sequence of serial changes in the primary
elements or data for a given HMM (over all disease states). Using
formula (9) for different HMMs, allows one to determine a model
(among several models), which gives the greatest probability of
observing a particular sequence of serial changes in the primary
elements or data.
It is also possible to construct a second-order Markov model in
which the probability of a certain disease (heart failure) state
will depend on 2 previous states (unlike in the 1.sup.st order
Markov model, where the probability of a state depends only on the
previous state). Obviously, the idea of the order of the model can
be generalized to any other number n=3, 4, 5 . . . N.
It is also possible to "train" HMM on data with known properties to
determine the optimal set of parameters (.THETA.={.pi., A, B}
defined above) that maximize the accuracy of the model with respect
to determination of a disease state or serial changes in disease
state (disease state sequence). The "training" goal could be
determined according to the specifics of a particular healthcare
application.
Note that the description of applications of the hidden Markov
models for analysis of the disease/health dynamics is not limited
to patients with implantable devices or patients with heart
failure. The Markov models and hidden Markov models, as well as
other Bayesian, probabilistic models/networks, can be used for
dynamic analysis of any disease state or health data in any
population or individual. These analytical tools can be also used
for tracking the probabilities of presence/changes in several
possible diseases (differential diagnosis).
Example VII
This theoretical example describes one of the applications of the
multi-scale monitoring system in a hospital setting for information
management, diagnosis, and decision support. A hypothetical patient
has been hospitalized with chest pain and an electrocardiographic
ST-depression with a tentative diagnosis of unstable angina. His
vital signs remained within physiologically normal limits. The
information from his ECG monitoring system, blood pressure, blood
count, biochemical examination, cardiac echocardiography, heart
computer tomography, and other tests has been transmitted into the
system from the respective systems and services (FIG. 1, bottom
scale). The analysis at Level I has confirmed presence of
ST-depression in the electrocardiogram. The analysis is Level II,
by comparing the present ECG with previous ECGs collected from the
same patient during previous hospital visits has showed that the
ST-depression has become much more pronounced, suggesting
developing of acute or unstable angina. In addition, the system
identified a subtle, gradual, but significant trend towards
increase in the QT-interval, which might indicate a heightened risk
of a cardiac arrhythmia. The abnormal trend in QT-intervals has
been highlighted as abnormal and potentially dangerous using
color-coding (red color) to draw attention of medical personnel.
Analysis at Level III also showed that a combination of a chest
pain, which has gradually intensified during the last 24 hours and
the ST-depression has never occurred before in this patient, also
confirmed presence of an acute coronary syndrome. At Level IV, the
system has used statistical data, pattern recognition, and
artificial intelligence to compare the patient's data with general
information about different diseases (from a medical knowledge base
available in a digital form on the network) that can cause chest
pain and ST-depression and determined the list of possible diseases
and stages of the diseases, with their respective probabilities.
This analysis confirmed that acute, unstable angina was the most
probable cause of the patient's current problem. As a result of the
system's analysis, the diagnosis has been confirmed within a very
short time, which resulted in an appropriate treatment initiated in
a timely fashion and helped avert potential complications, such as
myocardial infarction and arrhythmias.
Example VIII
This theoretical example describes one of the applications of the
multi-scale monitoring system in an out-hospital (ambulatory)
setting for information management, diagnosis, and decision
support. A hypothetical patient at home experienced a chest pain.
Using a personalized ECG system, the subject recorded his ECG and
sent it using a wireless (cell phone) transmission to a remote
computer center, where the patient's historic data has been stored.
This information along with the subject's complaint on the chest
paint has been entered in the information management system (FIG.
1, bottom). The system has processed the ECG (Level I) and
identified that all primary elements (amplitudes and durations of
all ECG complexes) are within normal range. At the next Level II,
the most current values of primary elements (amplitudes and
durations of P, Q, R, S, T-waves, QT-interval, QRS-complex,
ST-interval, T-wave alternans) were compared with historic data
obtained from the same subjects during previous ECG examinations.
The system did not identify any trends or changes in the data using
the comparative analysis of serial data. Thus, the results of
serial ECG analysis were highlighted with a green color, which
indicates a normal range and trend. At the Level III, the system
combined the patient's complains on chest pain with the
ECG-findings and compared with previous (historic) findings of
chest pain and ECG-patterns obtained from the same patient over a
3-year period. No changes were identified with respect to
previously recorded perception of chest pain or the ECG-pattern. At
the next Level IV, the system compared the findings with the
previously identified in this patient finding of degenerative disk
pathology in the thoracic part of the spine and with the general
medical knowledge base containing symptoms of different diseases.
This comparative analysis has determined that the most probable
cause of the patient's symptoms is related to the degenerative disk
disease. This information has been transmitted to the physician's
smart phone from the information management system wirelessly, and
the physician referred the patient to an orthopedic surgeon. Thus,
the information management system helped to avoid unnecessary
emergency visit into the Emergency Room and unnecessary diagnostic
tests and examinations, streamlined and reduced the time and cost
of the diagnosis.
In addition to the above-described orthogonal linear decomposition,
other methods of non-orthogonal decomposition or independent
component analysis, multidimensional scaling based on non-metric
distances and mapping techniques can be used for multi-scale
analysis. These include but are not limited to non-orthogonal
linear mappings, nonlinear mappings and other projection methods
that make use of such mathematical tools as the domain and range
straightening, and re-scaling (change of variables), methods from
the theories of singularities, bifurcations, catastrophes, and
dynamical systems. In addition, other statistical estimators, such
as a linear and nonlinear correlation, analysis of variance,
cluster analysis, factor analysis, canonical analysis, regression
and discriminant function analysis, and probabilistic methods, such
as Bayesian probability, pattern recognition, and methods of
artificial intelligence, including neural networks, fuzzy logic,
and expert systems, as well as hybrid (combined) artificial
intelligence systems, can be applied for estimating the temporal
changes in the physiological data and in the derived variables at
different scales (resolutions).
Further implementation of the multi-scale analysis is possible to
provide detailed characterization of serial changes using a fuzzy
logic classifier or a dynamic neural network with at least one
neuron (unit) analyzing changes in one or more states of activity
of at least one physiological, biochemical, biophysical,
mechanical, or genetic system relative to at least one reference
value. For example, such a system could be used to examine changes
in activities of the sympathetic and parasympathetic nervous
systems over short or long periods of time during sleep, physical,
or psychological tests. As another example, the above-described
system could be used to characterize dynamics of a chronic disease,
such as congestive heart failure, first, by analyzing changes in
each physiological indicator (such as heart rate, blood pressure,
or cardiac output) at rest and during various physical activities
in comparison with individual reference values (Scale I, II), and
second, by combining the results of Scale-I-and-II-analyses into a
general assessment of changes in the patient's condition (Scale
III). Furthermore, the reference values could be represented either
by a single parameter or by a relation (mathematical function or
statistical distribution) between said reference values and a state
or states of physiological, biochemical, biophysical, mechanical,
or genetic system. For instance, a reference value could represent
a range of changes in a physiological parameter, such as heart
rate, over 24 hours or during a stress test. Although these methods
are substantially different from each other, a novel, unifying
feature of the present invention is that the information is
processed at different scales (levels of resolution or details) and
that the different levels of processing can be distributed among
computers and devices on a network. Thus, in a framework of the
present invention, each of the above-described methods could be
implemented instead of the linear orthogonal decomposition for
multi-scale distributed analysis of physiological data, exchange of
the results between the scales, and representation of the results
of multi-scale analysis for lay people and medical professionals.
In particular, an artificial intelligence system (an expert system
or a neural network) can be implemented using a multi-layer
structure, in which each layer of processing rules or nodes
(elementary units on the neural net or objects in the expert
system) has a different processing resolution (scale). Thus, this
structure can have a low-resolution processing (Scale I) and a
higher-resolution processing scheme (Scales II and III), as
described by the present invention. Such artificial intelligence
systems could be used for the types of physiological data that
could be modeled by inter-connected nodes with elementary input and
output operations (a neural network) or could be represented by
informal (heuristic) rules of processing (an expert system), or
could be implemented in a combined system of rules and nodes (a
hybrid system). Although these methods are very general and widely
used in different applications, the present invention describes a
novel multi-resolution (multi-scale) structure of these systems and
its applications for dynamic analysis of subtle changes in health
data.
As another example, Mahalanobis distance, a measure of distance
between two points in the space defined by two or more, possibly,
correlated variables can be used to determine the probability of a
change in the physiological data at different scales. For each
variable, the location of the point mean steady-state value
(centroid 1) and the mean unsteady value (centroid 2) are
determined. Mahalanobis distances from the steady-state and the
unsteady centroids to each data point are then calculated. The
probability that a point belongs to the steady-state or the
unsteady sector is proportional to the Mahalanobis distance from
that sector centroid. These distances, for example, could be used
for the estimation of temporal changes in electrocardiographic
T-wave amplitude shown in FIG. 13. In particular, the probability
of a change in the new T-wave amplitude data at a low-resolution
scale can be determined using Mahalanobis distance between the new
data and the two centroids (steady-state and unsteady one). At the
higher-resolution scale, the probability of a change, its
magnitude, and other characteristics could be estimated more
precisely by separating the steady-state and the unsteady sectors
into sub-sectors, determining the corresponding centroids, and
estimating Mahalanobis distances between the new data and the
centroid of each sub-sector. The locations of the centroids are
updated after the new data are collected to provide time-adjusted,
individual reference or baseline values. The distances between the
centroids demonstrate the individual range of variations in the
studied variables, which can be compared to the average values in a
group or a population. Mahalanobis distances can also be used to
estimate the changes in combinations of variables.
This procedure is similar to the inclusion of additional dimensions
(components) into the PCA. However, unlike PCA, the nonlinear
estimation or an artificial intelligence approach is not limited to
orthogonal components and metric distances, but may include
non-orthogonal components (also referred to as the independent
components) and nonlinear estimators.
It is therefore seen that this invention provides a physiological
data analysis system and method for detecting a plurality of
primary elements and comparing the detected elements with reference
or baseline values both quantitatively and qualitatively. The
outputs from the system in both low level resolution and higher
levels of resolution can be understood by both lay persons and
medical professionals. The system includes means for exchanging
information and direction from an external computer for analysis
and modification of the low resolution analysis of the signal. The
system further includes mathematical methods and applications
described in Shusterman's U.S. Pat. Nos. 6,389,308 and 6,925,324,
which are incorporated herein by reference.
Whereas particular aspects of the method of the present invention
and particular embodiments of the invention have been described for
purposes of illustration, it will be appreciated by those skilled
in the art that numerous variations of the details may be made
without departing from the invention as described in the appended
claims.
* * * * *